In the file each article is seperated by 'NEW_PAPER' in 'covid-scienc.txt' and 'NEW NEWSPAPER' in 'covid-non-science'.
#import
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning. warnings.warn(msg)
def load_data(path,file_name,seperator):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
document = ''
i=0
with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for line in fin.readlines():
text = line.strip()
i=i+1
if text !='':
document = document+text
if text == seperator:
documents_list.append(document)
document = ''
documents_list.append(document)
print("Total Number of Documents:",len(documents_list))
titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count=load_data("","covid-science.txt",'NEW_PAPER')
Number of words in text file : 120257 Total Number of Documents: 20
def preprocess_data(doc_set):
"""
Input : docuemnt list
Purpose: preprocess text (tokenize, removing stopwords, and stemming)
Output : preprocessed text
"""
# initialize regex tokenizer
tokenizer = RegexpTokenizer(r'\w+')
# create English stop words list
en_stop = set(stopwords.words('english'))
# Create p_stemmer of class PorterStemmer
p_stemmer = PorterStemmer()
# list for tokenized documents in loop
texts = []
# loop through document list
for i in doc_set:
# clean and tokenize document string
raw = i.lower()
tokens = tokenizer.tokenize(raw)
tokens = [i for i in tokens if len(i)>4]
print(tokens)
# remove stop words from tokens
stopped_tokens = [i for i in tokens if not i in en_stop]
# stem tokens
stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]
# add tokens to list
texts.append(stemmed_tokens)
return texts
clean_text=preprocess_data(document_list)
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def prepare_corpus(doc_clean):
"""
Input : clean document
Purpose: create term dictionary of our courpus and Converting list of documents (corpus) into Document Term Matrix
Output : term dictionary and Document Term Matrix
"""
# Creating the term dictionary of our courpus, where every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
dictionary = corpora.Dictionary(doc_clean)
# Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]
# generate LDA model
return dictionary,doc_term_matrix
def create_gensim_lsa_model(doc_clean,number_of_topics,words):
"""
Input : clean document, number of topics and number of words associated with each topic
Purpose: create LSA model using gensim
Output : return LSA model
"""
dictionary,doc_term_matrix=prepare_corpus(doc_clean)
# generate LSA model
lsamodel = LsiModel(doc_term_matrix, num_topics=number_of_topics, id2word = dictionary) # train model
#print(lsamodel.print_topics(num_topics=number_of_topics, num_words=words))
return lsamodel
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'covid': 0.5195000201940307,
'imag': 0.3819419924959869,
'model': 0.2441435717675279,
'dataset': 0.1784613020969509,
'class': 0.1529705981881784}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph12.png')
# LSA Model
number_of_topics = 20 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled15.png')
cosine-similarity
covid imag model dataset class
covid 1 0.114718 0.0699065 -0.0721506 0.275795
imag 0.114718 1 -0.101632 0.179926 0.0364016
model 0.0699065 -0.101632 1 0.00335218 0.049745
dataset -0.0721506 0.179926 0.00335218 1 0.0568222
class 0.275795 0.0364016 0.049745 0.0568222 1
document_list,titles,word_count=load_data("","covid-non-science.txt",'NEW NEWSPAPER')
Number of words in text file : 8713 Total Number of Documents: 20
clean_text=preprocess_data(document_list)
['covid', 'recovery', 'covid', 'realityby', 'reuterspublished', 'updated', '2020by', 'joneslondon', 'reuters', 'world', 'shares', 'inched', 'towards', 'month', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'nagging', 'coronavirus', 'nerves', 'market', 'rally', 'fuelled', 'record', 'numbers', 'largely', 'blown', 'itself', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'decade', 'stimulus', 'ensured', 'optimism', 'remained', 'chinese', 'shares', 'charged', 'their', 'highest', 'level', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'traders', 'surprise', 'currency', 'commodity', 'markets', 'subdued', 'after', 'otherwise', 'strong', 'confidence', 'sensitive', 'stalwarts', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday', 'think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'doused', 'enthusiasm', 'societe', 'generale', 'strategist', 'jukes', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'places', 'return', 'infection', 'rates', 'which', 'nervous', 'against', 'basket', 'currencies', 'dollar', 'slightly', 'early', 'london', 'trading', 'still', 'firmly', 'track', 'biggest', 'weekly', 'since', 'first', 'though', 'gained', 'against', 'swiss', 'franc', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown', 'futures', 'volumes', 'lower', 'usual', 'markets', 'holiday', 'friday', 'independence', 'nonfarm', 'payrolls', 'surged', 'million', 'above', 'average', 'forecast', 'million', 'thanks', 'rises', 'hospitality', 'sectors', 'economists', 'noted', 'there', 'caveats', 'upbeat', 'headline', 'figures', 'number', 'permanent', 'losers', 'continued', 'increasing', 'million', 'while', 'unemployment', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'analysis', 'unemployment', 'behind', 'developed', 'market', 'peers', 'barring', 'canada', 'recovery', 'faces', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'cases', 'reverse', 'plans', 'stores', 'reopen', 'activities', 'resume', 'three', 'dozen', 'states', 'increases', 'covid', 'cases', 'cases', 'florida', 'spiking', 'above', 'nevertheless', 'markets', 'largely', 'overlooking', 'spikes', 'taking', 'overall', 'situation', 'still', 'improving', 'overall', 'german', 'government', 'yields', 'basis', 'points', 'their', 'biggest', 'weekly', 'month', 'though', 'nudged', 'friday', 'riskier', 'italian', 'yields', 'though', 'which', 'their', 'lowest', 'since', 'march', 'prices', 'eased', 'after', 'otherwise', 'solid', 'brent', 'crude', 'barrel', 'while', 'crude', 'dropped', 'barrel', 'around', 'months', 'copper', 'prices', 'poised', 'seventh', 'consecutive', 'weekly', 'their', 'longest', 'winning', 'streak', 'nearly', 'three', 'years', 'despite', 'slight', 'easing', 'after', 'supplier', 'chile', 'assured', 'traders', 'about', 'supply', 'three', 'month', 'copper', 'hovering', 'tonne', 'ploughed', 'march', 'issue', 'hangs', 'markets', 'surge', 'secondary', 'infections', 'trigger', 'second', 'national', 'rather', 'regional', 'shutdowns', 'malcolm', 'freeman', 'director', 'kingdom', 'futures', 'wrote', 'reporting', 'jones', 'newspaper'] ['covid', 'vaccine', 'frontrunnersby', 'reuterspublished', 'october', 'updated', 'october', '2020oct', 'reuters', 'pfizer', 'could', 'november', 'authorization', 'covid', 'vaccine', 'developing', 'opening', 'possibility', 'vaccine', 'being', 'available', 'united', 'states', 'moderna', 'astrazeneca', 'close', 'behind', 'largest', 'drugmaker', 'likely', 'early', 'their', 'vaccine', 'candidates', 'before', 'potential', 'vaccines', 'being', 'developed', 'tested', 'globally', 'covid', 'pandemic', 'human', 'trials', 'according', 'world', 'health', 'organization', 'following', 'tracks', 'candidates', 'final', 'stages', 'testing', 'separate', 'factbox', 'coronavirus', 'vaccines', 'under', 'development', 'click', 'company', 'stage', 'development', 'expected', 'doses', 'pfizer', 'stage', 'trials', 'underway', 'option', 'biontech', 'pfizer', 'earlier', 'october', 'could', 'emergency', 'authorization', 'november', 'indicating', 'vaccine', 'could', 'potentially', 'available', 'additional', 'canada', 'specified', 'japan', 'moderna', 'completed', 'enrollment', 'option', 'subjects', 'stage', 'trial', 'additional', 'interim', 'results', 'trial', 'expected', 'november', 'japan', 'distributed', 'takeda', 'canada', 'specified', 'johnson', 'johnson', 'large', 'vaccine', 'trial', 'resume', 'after', 'paused', 'under', 'subsequent', 'agreement', 'illness', 'study', 'volunteer', 'results', 'trial', 'expected', 'option', 'early', 'additional', 'canada', 'japan', 'applicable', 'astrazeneca', 'stage', 'trials', 'underway', 'supply', 'deals', 'countries', 'oxford', 'university', 'trial', 'vaccine', 'candidate', 'doses', 'pause', 'since', 'after', 'patient', 'trial', 'results', 'expected', 'coming', 'weeks', 'commitment', 'deliver', 'total', 'least', 'additional', 'italy', 'germany', 'netherlands', 'france', 'additional', 'canada', 'japan', 'sinovac', 'biotech', 'stage', 'trials', 'brazil', 'vaccine', 'approved', 'emergency', 'indonesia', 'turkey', 'underway', 'china', 'supply', 'prelim', 'expected', 'november', 'indonesia', 'least', 'doses', 'before', 'gamaleya', 'research', 'stage', 'trial', 'covid', 'people', 'inoculated', 'institute', 'vaccine', 'sputnik', 'underway', 'russia', 'supply', 'deals', 'since', 'prelim', 'countries', 'including', 'india', 'expected', 'november', 'brazil', 'saudi', 'arabia', 'doses', 'cansino', 'biologics', 'vaccine', 'candidate', 'final', 'stage', 'mexico', 'doses', 'early', 'trials', 'approved', 'chinese', 'military', 'stage', 'trial', 'underway', 'pakistan', 'sinopharm', 'group', 'stage', 'trials', 'expects', 'produce', 'underway', 'billion', 'doses', 'reporting', 'dania', 'nadeem', 'mrinalika', 'bengaluru', 'editing', 'ankur', 'banerjee', 'samuel', 'newspaper'] ['giants', 'another', 'covid', 'caseby', 'reuterspublished', 'november', 'updated', 'november', '2020the', 'giants', 'another', 'covid', 'monday', 'night', 'notified', 'player', 'tested', 'positive', 'covid', 'giants', 'announced', 'tuesday', 'morning', 'player', 'immediately', 'isolated', 'contact', 'tracing', 'process', 'initiated', 'close', 'contacts', 'staff', 'members', 'identified', 'informed', 'remain', 'today', 'giants', 'entering', 'their', 'second', 'player', 'positive', 'covid', 'prior', 'playing', 'tampa', 'buccaneers', 'offensive', 'lineman', 'hernandez', 'tested', 'positive', 'hernandez', 'spent', 'nearly', 'weeks', 'reserve', 'covid', 'returned', 'played', 'sunday', 'victory', 'philadelphia', 'eagles', 'field', 'level', 'medianew', 'newspaper'] ['covid', 'antivirals', 'taskforce', 'launchedby', 'mediapublished', 'april', 'updated', 'april', '2021at', 'covid', 'treatments', 'being', 'sought', 'government', 'experts', 'speed', 'recovery', 'people', 'become', 'infected', 'virus', 'government', 'taskforce', 'supercharge', 'search', 'antiviral', 'medications', 'covid', 'prime', 'minister', 'boris', 'johnson', 'group', 'medicines', 'covid', 'tracks', 'hoped', 'antivirals', 'could', 'reduce', 'infections', 'limit', 'impact', 'variants', 'protect', 'people', 'cannot', 'vaccines', 'those', 'fully', 'protected', 'after', 'having', 'officials', 'bring', 'treatments', 'online', 'could', 'people', 'covid', 'those', 'close', 'contact', 'could', 'offered', 'antiviral', 'medication', 'offered', 'tablet', 'infection', 'spreading', 'speed', 'recovery', 'antivirals', 'taskforce', 'which', 'appoint', 'chair', 'search', 'promising', 'potential', 'antiviral', 'medicines', 'which', 'taken', 'taskforce', 'support', 'development', 'drugs', 'through', 'clinical', 'trials', 'manufacturing', 'opportunities', 'antiviral', 'drugs', 'medication', 'specifically', 'treating', 'viral', 'infections', 'killing', 'preventing', 'growth', 'viruses', 'success', 'vaccination', 'programme', 'demonstrated', 'achieve', 'bring', 'together', 'brightest', 'minds', 'johnson', 'antivirals', 'taskforce', 'develop', 'innovative', 'treatments', 'covid', 'tracks', 'these', 'could', 'provide', 'another', 'vital', 'defence', 'against', 'future', 'increase', 'infections', 'lives', 'health', 'secretary', 'hancock', 'medicines', 'vital', 'weapon', 'protect', 'loved', 'terrible', 'virus', 'modelled', 'success', 'vaccines', 'therapeutics', 'taskforces', 'which', 'played', 'crucial', 'response', 'pandemic', 'bringing', 'together', 'supercharge', 'search', 'antiviral', 'treatments', 'autumn', 'government', 'chief', 'scientific', 'adviser', 'patrick', 'vallance', 'speed', 'which', 'vaccines', 'therapeutics', 'dexamethasone', 'identified', 'deployed', 'against', 'covid', 'critical', 'pandemic', 'response', 'antivirals', 'tablet', 'another', 'response', 'could', 'protect', 'those', 'protected', 'ineligible', 'vaccines', 'could', 'another', 'layer', 'defence', 'variants', 'concern', 'nikita', 'kanani', 'medical', 'director', 'primary', 'england', 'research', 'reality', 'record', 'speed', 'during', 'pandemic', 'taskforce', 'identify', 'convenient', 'treatments', 'patients', 'covid', 'commenting', 'announcement', 'penny', 'visiting', 'professor', 'pharmaceutical', 'medicine', 'kings', 'college', 'london', 'antiviral', 'treatment', 'influenza', 'shown', 'reduce', 'hospitalisation', 'prevent', 'death', 'epidemic', 'pandemic', 'waves', 'similar', 'preferably', 'simple', 'antiviral', 'suitable', 'community', 'range', 'interventions', 'critical', 'enable', 'remain', 'covid', 'pandemic', 'event', 'emergence', 'viral', 'variants', 'insensitive', 'vaccines', 'there', 'number', 'early', 'phase', 'trials', 'looking', 'antiviral', 'medications', 'newspaper'] ['african', 'covid', 'deaths', '000by', 'afppublished', 'february', 'updated', 'february', '2021africa', 'thursday', 'recorded', 'deaths', 'covid', 'milestone', 'likely', 'understate', 'continent', 'billion', 'people', 'battles', 'second', 'infections', 'countries', 'region', 'death', 'reported', 'cases', 'according', 'tally', 'continent', 'relatively', 'spared', 'pandemic', 'except', 'oceania', 'reach', 'threshold', 'deaths', 'which', 'europe', 'crossed', 'april', 'south', 'africa', 'worst', 'african', 'country', 'rolled', 'testing', 'campaign', 'start', 'pandemic', 'country', 'recorded', 'nearly', 'million', 'cases', 'deaths', 'those', 'figures', 'based', 'daily', 'reports', 'communicated', 'health', 'authorities', 'reflect', 'fraction', 'actual', 'health', 'specialists', 'cases', 'clearly', 'under', 'reported', 'because', 'access', 'healthcare', 'facilities', 'under', 'reporting', 'milder', 'cases', 'south', 'african', 'virologist', 'barry', 'schoub', 'member', 'scientific', 'council', 'south', 'african', 'ministry', 'health', 'understaffed', 'health', 'facilities', 'means', 'meant', 'african', 'countries', 'unable', 'testing', 'countries', 'mainly', 'tests', 'capitals', 'further', 'moves', 'urban', 'centres', 'there', 'tests', 'explained', 'french', 'epidemiologist', 'emmanuel', 'baron', 'doctors', 'without', 'borders', 'disease', 'unnoticed', 'asymptomatic', 'patients', 'symptoms', 'confused', 'others', 'added', 'covid', 'found', 'pawpaw', 'zimbabwe', 'country', 'devastated', 'economy', 'mismanaged', 'health', 'system', 'hospitals', 'filled', 'covid', 'patients', 'exhausted', 'doctors', 'overwhelmed', 'nurses', 'official', 'number', 'cases', 'remains', 'tanzania', 'stopped', 'testing', 'after', 'claiming', 'found', 'positive', 'covid', 'pawpaw', 'quail', 'tanzanian', 'government', 'released', 'official', 'figures', 'april', 'someone', 'continent', 'would', 'deaths', 'infection', 'probably', 'would', 'believed', 'nkengasong', 'africa', 'director', 'centers', 'disease', 'control', 'preventions', 'reporters', 'thursday', 'africa', 'however', 'significantly', 'lower', 'europe', 'world', 'affected', 'region', 'deaths', 'recorded', 'other', 'regions', 'badly', 'latin', 'america', 'deaths', 'united', 'states', 'canada', 'deaths', 'after', 'sharp', 'increase', 'january', 'africa', 'figures', 'fallen', 'sharply', 'weeks', 'seven', 'continent', 'recorded', 'deaths', 'percent', 'previous', 'height', 'pandemic', 'january', 'continent', 'deaths', 'disaster', 'while', 'coronavirus', 'figures', 'clearly', 'underestimated', 'health', 'disaster', 'africa', 'baron', 'several', 'studies', 'antibodies', 'which', 'possible', 'detect', 'whether', 'person', 'recovered', 'previously', 'exposed', 'virus', 'underway', 'african', 'countries', 'should', 'provide', 'better', 'impact', 'pandemic', 'region', 'south', 'africa', 'where', 'almost', 'latest', 'cases', 'attributed', 'variant', 'virus', 'known', 'contagious', 'which', 'spread', 'widely', 'represents', 'nearly', 'deaths', 'reported', 'cases', 'continent', 'other', 'african', 'countries', 'affected', 'egypt', 'deaths', 'cases', 'morocco', 'deaths', 'cases', 'south', 'africa', 'country', 'covid', 'fatalities', 'continent', 'counting', 'deaths', 'inhabitants', 'ahead', 'tunisia', 'deaths', 'eswatini', 'lagging', 'behind', 'vaccination', 'continent', 'leading', 'industrial', 'powerhouse', 'administered', 'first', 'vaccines', 'wednesday', 'globally', 'covid', 'caused', 'million', 'infections', 'million', 'deaths', 'since', 'start', 'epidemic', 'wuhan', 'china', 'december', 'harnew', 'newspaper'] ['covid', 'hotspots', 'industryby', 'australian', 'associated', 'presspublished', 'august', 'updated', 'august', '2020the', 'fitness', 'industry', 'perception', 'covid', 'hotspots', 'saying', 'there', 'transmission', 'virus', 'linked', 'fitness', 'australia', 'chief', 'executive', 'barrie', 'elvish', 'fitness', 'australia', 'gathered', 'random', 'sample', 'operators', 'proves', 'since', 'reopened', 'there', 'million', 'visits', 'recorded', 'community', 'transmission', 'cannot', 'restaurants', 'where', 'cluster', 'cases', 'started', 'spread', 'however', 'still', 'being', 'categorised', 'these', 'other', 'entertainment', 'venues', 'negative', 'light', 'statement', 'thursday', 'however', 'misinformation', 'negative', 'commentary', 'portrayed', 'health', 'fitness', 'facilities', 'hotspots', 'covid', 'resulting', 'people', 'being', 'scared', 'exercise', 'their', 'despite', 'additional', 'precautions', 'stringent', 'hygiene', 'practices', 'fitness', 'australia', 'gathered', 'electronic', 'swipes', 'members', 'access', 'which', 'could', 'sophisticated', 'contact', 'tracing', 'should', 'required', 'elvish', 'there', 'reported', 'cases', 'where', 'member', 'visited', 'while', 'infected', 'covid', 'health', 'authorities', 'identified', 'cases', 'transmission', 'problem', 'actually', 'solution', 'getting', 'australians', 'active', 'often', 'prevent', 'lifestyle', 'related', 'disease', 'elvish', 'urging', 'government', 'fitness', 'australia', 'industry', 'ensure', 'health', 'safety', 'community', 'newspaper'] ['covid', 'victims', 'maintain', 'immunityby', 'australian', 'associated', 'presspublished', 'november', 'updated', 'november', '2020australian', 'researchers', 'discovered', 'patients', 'infected', 'covid', 'retain', 'immunity', 'against', 'virus', 'disease', 'least', 'eight', 'months', 'research', 'strongest', 'evidence', 'vaccines', 'against', 'virus', 'periods', 'previous', 'studies', 'found', 'first', 'antibodies', 'produced', 'human', 'after', 'infection', 'waned', 'after', 'first', 'months', 'raising', 'concerns', 'people', 'could', 'quickly', 'immunity', 'research', 'allays', 'those', 'concerns', 'study', 'result', 'collaboration', 'associate', 'professor', 'menno', 'monash', 'university', 'published', 'monday', 'preprint', 'server', 'medrxiv', 'researchers', 'found', 'specific', 'within', 'human', 'immune', 'system', 'memory', 'remembers', 'infection', 'virus', 'challenged', 'again', 'through', 'exposure', 'virus', 'triggers', 'protective', 'immune', 'response', 'through', 'rapid', 'production', 'protective', 'antibodies', 'researchers', 'recruited', 'covid', 'patients', 'blood', 'samples', 'between', 'infection', 'infection', 'other', 'studies', 'looking', 'antibody', 'response', 'researchers', 'found', 'antibodies', 'against', 'virus', 'started', 'after', 'infection', 'however', 'patients', 'continued', 'memory', 'cells', 'recognised', 'components', 'virus', 'spike', 'nucleocapsid', 'proteins', 'these', 'virus', 'specific', 'memory', 'cells', 'present', 'eight', 'months', 'after', 'infection', 'associate', 'professor', 'results', 'efficacy', 'vaccine', 'against', 'virus', 'explained', 'there', 'examples', 'genuine', 'reinfection', 'millions', 'people', 'tested', 'positive', 'virus', 'globally', 'these', 'results', 'important', 'because', 'definitively', 'patients', 'infected', 'covid', 'virus', 'retain', 'immunity', 'against', 'virus', 'disease', 'black', 'cloud', 'hanging', 'potential', 'protection', 'could', 'provided', 'covid', 'vaccine', 'gives', 'vaccine', 'vaccines', 'developed', 'provide', 'protection', 'newspaper'] ['origins', 'covid', 'reuterspublished', 'january', 'updated', 'january', '2021shanghai', 'reuters', 'world', 'health', 'organization', 'works', 'china', 'investigate', 'origins', 'covid', 'following', 'factbox', 'looks', 'about', 'pandemic', 'began', 'china', 'originsthe', 'coronavirus', 'causes', 'covid', 'known', 'first', 'identified', 'central', 'chinese', 'wuhan', 'january', 'scientists', 'believe', 'still', 'likely', 'originated', 'china', 'peter', 'embarek', 'expert', 'safety', 'zoonotic', 'diseases', 'wuhan', 'offered', 'first', 'solid', 'clues', 'about', 'transmission', 'covid', 'investigation', 'would', 'start', 'there', 'though', 'studies', 'suggested', 'covid', 'present', 'italy', 'spain', 'earlier', 'might', 'responsible', 'spike', 'pneumonia', 'cases', 'france', 'researchers', 'believe', 'could', 'entered', 'wuhan', 'europe', 'closest', 'relative', 'nature', 'ratg13', 'virus', 'which', 'discovered', 'horseshoe', 'southwest', 'china', 'yunnan', 'province', 'genetic', 'match', 'between', 'makes', 'highly', 'likely', 'covid', 'originated', 'colonies', 'china', 'southwest', 'border', 'regions', 'huanan', 'marketthe', 'initial', 'cluster', 'infections', 'traced', 'huanan', 'seafood', 'market', 'wuhan', 'leading', 'assume', 'patient', 'probably', 'trader', 'exposed', 'contaminated', 'products', 'version', 'events', 'simplistic', 'explain', 'patterns', 'infection', 'wuhan', 'elsewhere', 'earliest', 'reported', 'cases', 'connection', 'market', 'wuhan', 'residents', 'hospitalised', 'turned', 'covid', 'huanan', 'chinese', 'researchers', 'there', 'types', 'circulating', 'wuhan', 'which', 'associated', 'market', 'scientists', 'virus', 'unusually', 'adapted', 'rapid', 'human', 'transmission', 'making', 'unlikely', 'first', 'human', 'contact', 'seafood', 'market', 'intermediary', 'species', 'investigators', 'intermediary', 'species', 'allowed', 'original', 'humans', 'preliminary', 'scientific', 'papers', 'china', 'identified', 'snakes', 'potential', 'candidates', 'similar', 'coronavirus', 'infections', 'found', 'pangolins', 'illegally', 'trafficked', 'china', 'scholars', 'believe', 'there', 'intermediary', 'species', 'virus', 'transmitted', 'directly', 'humans', 'possibly', 'multiple', 'occasions', 'first', 'people', 'infected', 'likely', 'traders', 'droppings', 'traditional', 'chinese', 'medicine', 'could', 'carried', 'huanan', 'seafood', 'market', 'causing', 'superspreader', 'event', 'allowed', 'pandemic', 'begin', 'function', 'though', 'there', 'credible', 'supporting', 'evidence', 'researchers', 'still', 'possibility', 'virus', 'released', 'accidentally', 'specialist', 'wuhan', 'institute', 'virology', 'there', 'indication', 'contains', 'synthetic', 'insertions', 'researchers', 'could', 'subject', 'process', 'known', 'function', 'where', 'forces', 'virus', 'become', 'infectious', 'exposing', 'human', 'receptor', 'cells', 'experts', 'investigators', 'needs', 'granted', 'access', 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'safework', 'conducted', 'covid', 'safety', 'visits', 'issued', 'penalties', 'worth', 'temporarily', 'closed', 'businesses', 'newspaper'] ['tests', 'underway', 'covid', 'treatmentby', 'australian', 'associated', 'presspublished', 'august', 'updated', 'august', '2020australian', 'researchers', 'testing', 'treatment', 'coronavirus', 'provide', 'relief', 'those', 'infected', 'covid', 'limit', 'spread', 'professor', 'david', 'morris', 'international', 'adapted', 'cancer', 'treatment', 'known', 'bromac', 'could', 'prevent', 'virus', 'moving', 'lungs', 'carriers', 'spreading', 'other', 'people', 'trial', 'coronavirus', 'patients', 'being', 'established', 'melbourne', 'hospital', 'could', 'start', 'month', 'bromac', 'under', 'development', 'years', 'cancer', 'treatment', 'includes', 'components', 'together', 'dissolve', 'spike', 'covid', 'rendering', 'unable', 'infect', 'other', 'cells', 'agents', 'pineapple', 'enzyme', 'tested', 'after', 'observed', 'eating', 'pineapples', 'resistant', 'particular', 'gastro', 'conditions', 'taken', 'development', 'decade', 'asked', 'whether', 'adapted', 'treating', 'people', 'infected', 'covid', 'professor', 'david', 'morris', 'statement', 'monday', 'results', 'renders', 'covid', 'spike', 'ineffective', 'stopping', 'infecting', 'other', 'cells', 'results', 'treatment', 'confine', 'covid', 'throat', 'prevent', 'infection', 'infected', 'patients', 'passing', 'virus', 'hoped', 'infected', 'person', 'could', 'medication', 'first', 'signs', 'covid', 'virus', 'tracks', 'could', 'provide', 'effective', 'saving', 'lives', 'protecting', 'vulnerable', 'workers', 'preventing', 'hospitalisation', 'getting', 'people', 'infected', 'covid', 'returning', 'their', 'everyday', 'lives', 'researchers', 'stress', 'treatment', 'vaccine', 'covid', 'could', 'tandem', 'treatment', 'prevention', 'measure']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science = dict(model.show_topic(0, topn=words))#dict and encoding matrix values
dict(model.show_topic(0,topn=5))
{'test': 0.5525012615032754,
'peopl': 0.32711032908806614,
'covid': 0.32350304714824646,
'symptom': 0.2014729774269745,
'travel': 0.15909033106510684}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph13.png')
# LSA Model
number_of_topics = 20 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_non_science =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_non_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled16.png')
cosine-similarity
test peopl covid symptom travel
test 1 0.336661 0.0349857 0.898669 0.716668
peopl 0.336661 1 0.308412 0.423709 0.271003
covid 0.0349857 0.308412 1 -0.0604921 -0.0986359
symptom 0.898669 0.423709 -0.0604921 1 0.755495
travel 0.716668 0.271003 -0.0986359 0.755495 1
document_list,titles,word_count=load_data("","covid-psychology.txt",'NEW PAPER')
Number of words in text file : 86550 Total Number of Documents: 20
clean_text=preprocess_data(document_list)
['international', 'sociology2021', 'author', '2020article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissionsdoi', '0268580920948807journals', 'sagepub', 'isspsychology', 'politics', 'ofcovid', 'misinfodemics', 'peoplebelieve', 'misinfodemics', 'sonia', 'mukhtaruniversity', 'management', 'technology', 'lahore', 'pakistanabstractmisinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adversehealth', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicateevidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'ofmisinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevierabout', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'thisreview', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'articleshows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'apsychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'thecrisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'thedevelopment', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywordsconspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'sciencethe', 'mechanisms', 'misinfodemicsin', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surroundingcorresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', 'com948807iss0010', '0268580920948807international', 'sociologymukhtarresearch', 'article2020article112', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modernglobalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origindue', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'duringlockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'hasbecome', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'forhealthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerablemisinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science', 'intimes', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'falsecontext', 'manipulated', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease', 'mukhtar', '2020a', 'covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms', 'havealso', 'become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated', 'bythese', 'rumors', 'caused', 'several', 'mishaps', 'across', 'world', 'instance', 'hisown', 'after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'overdosed', 'onthe', 'cholorquine', 'after', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', 'there', 'failure', 'makea', 'distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmedanti', 'science', 'conspiracy', 'theories', 'among', 'general', 'public', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'vaccine', 'covid', 'canalso', 'create', 'social', 'stigma', 'resulting', 'xenophobia', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'adverse', 'healthand', 'psychosocial', 'impacts', 'aguilera', '2020a', '2020b', 'these', 'considerations', 'become', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe', 'ofpopular', 'opinion', 'spend', 'social', 'media', 'people', 'trying', 'tomake', 'sense', 'their', 'changed', 'lives', 'proclaimed', 'celebrities', 'identified', 'socialmedia', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'theirown', 'subjective', 'interpretations', 'events', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer', 'scientific', 'studies', 'previous', 'disease', 'outbreaks', 'demonstrated', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', 'kalichman', 'individuals', 'endorsemisinformation', 'about', 'disease', 'likely', 'follow', 'public', 'health', 'instructions', 'duringthe', 'ebola', 'outbreak', 'respondents', 'credence', 'conspiracy', 'theoriesalleged', 'would', 'likely', 'support', 'during', 'disease', 'outbreak', 'national', 'cancer', 'institute', 'study', 'montanaro', 'conducted', 'theus', 'respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'informationabout', 'covid', 'current', 'administration', 'mainstream', 'media', 'outlets', 'times', 'mukhtar', '113the', 'psychology', 'misinfodemicsfear', 'adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actualor', 'perceived', 'threat', 'mertens', 'however', 'excessive', 'thismay', 'detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'anxiety', 'andphobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia', 'isinsufficient', 'cause', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation', 'ofpolicies', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonalsafety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocialissues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact', 'efforts', 'misinfodemics', 'process', 'increased', 'because', 'adverseeffects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainablepreventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health', 'andresilience', 'socioeconomic', 'conditions', 'address', 'stigma', 'prejudice', 'discriminationand', 'inequalities', 'mukhtar', '2020b', 'among', 'various', 'factors', 'psychological', 'vulnerability', 'mukhtar', '2020c', 'mukhtar', 'mahmood', 'propensity', 'follow', 'popularopinion', 'excessive', 'social', 'media', 'presence', 'predisposition', 'anxiety', 'touncertainty', 'intolerance', 'unpredictability', 'coronavirus', 'brings', 'levels', 'uncertainty', 'inability', 'uncertainty', 'exponentially', 'higher', 'causing', 'anxiety', 'health', 'related', 'worry', 'situation', 'exacerbated', 'exposure', 'plethoraof', 'information', 'including', 'misinformation', 'disinformation', 'about', 'impendingthreat', 'mainstream', 'media', 'social', 'media', 'alike', 'rosser', 'perceived', 'threatinformation', 'elevates', 'repeated', 'engagement', 'trauma', 'related', 'media', 'contentfor', 'several', 'hours', 'daily', 'culminates', 'acute', 'stress', 'emotional', 'distress', 'consequentlywill', 'either', 'increase', 'virus', 'cause', 'insensitivity', 'towards', 'course', 'identification', 'monitoring', 'internet', 'centrifugal', 'clarification', 'filter', 'theaccuracy', 'content', 'become', 'challenging', 'increasing', 'number', 'people', 'relyon', 'social', 'media', 'platforms', 'challenging', 'problems', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'social', 'media', 'emulsify', 'false', 'misleading', 'click', 'content', 'world', 'health', 'organization', 'launched', 'mythbuster', 'feature', 'website', 'countermeasure', 'spread', 'unauthenticnews', 'social', 'media', 'people', 'reported', 'highest', 'media', 'exposure', 'reported', 'higheracute', 'stress', 'holman', 'media', 'exposure', 'accumulates', 'emerging', 'threatsand', 'repeated', 'exposure', 'these', 'events', 'increase', 'symptoms', 'distress', 'worry', 'fixation', 'mentality', 'misplaced', 'conviction', 'people', 'vulnerable', 'media', 'exposure', 'distress', 'alongside', 'amount', 'media', 'exposure', 'content', 'exposure', 'matters', 'aswell', 'exposure', 'tragic', 'events', 'graphic', 'images', 'conspiracy', 'theories', 'violence', 'couldinstigate', 'posttraumatic', 'stress', 'future', 'which', 'personalfunctioning', 'recent', 'years', 'mainstream', 'media', 'electronic', 'print', 'social', 'media', 'established', 'themselves', 'beyond', 'government', 'channel', 'global114', 'international', 'sociology', 'population', 'developed', 'affinity', 'channels', 'particular', 'while', 'olderpopulation', 'mainly', 'relies', 'traditional', 'channels', 'youth', 'social', 'mediasuch', 'tiktok', 'instagram', 'youtube', 'twitter', 'facebook', 'whatsapp', 'social', 'mediahave', 'become', 'conduit', 'spreading', 'rumors', 'deliberate', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'personally', 'motivated', 'anecdotes', 'appeal', 'followersand', 'attract', 'attention', 'create', 'panic', 'liang', 'proclaimed', 'celebrities', 'media', 'stars', 'become', 'influential', 'large', 'number', 'people', 'solelyon', 'their', 'content', 'merchant', 'social', 'media', 'posts', 'circulating', 'manyplatforms', 'endorsed', 'multiple', 'social', 'media', 'account', 'users', 'encourage', 'optimalpersonal', 'functioning', 'maximum', 'productivity', 'business', 'oriented', 'activities', 'downplaying', 'intensity', 'traumatic', 'event', 'certain', 'posts', 'ofthis', 'quarantine', 'skill', 'hustle', 'started', 'knowledge', 'younever', 'lacked', 'lacked', 'discipline', 'circulating', 'various', 'social', 'media', 'accounts', 'prompting', 'people', 'utilize', 'their', 'learning', 'things', 'skills', 'exacerbate', 'theworry', 'already', 'anxiety', 'prone', 'individuals', 'place', 'psychological', 'pressure', 'productive', 'rather', 'messages', 'quarantine', 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'followers', 'subscribers', 'media', 'accounts', 'anddownplaying', 'trauma', 'others', 'devastating', 'effects', 'mentalwellbeing', 'lockdown', 'resulting', 'isolation', 'quarantine', 'social', 'distancing', 'removedfrom', 'normal', 'leisure', 'might', 'utilized', 'improved', 'personal', 'functioning', 'acollective', 'traumatic', 'event', 'which', 'poses', 'serious', 'threat', 'people', 'resulted', 'hugeloss', 'lives', 'displacement', 'individuals', 'mukhtar', 'mukhtar', 'mukhtarand', 'covid', 'individual', 'collective', 'traumatic', 'event', 'directly', 'orindirectly', 'affected', 'every', 'individual', 'world', 'efforts', 'should', 'directed', 'towardsminimizing', 'negative', 'effects', 'traumatic', 'covid', 'pandemic', 'survivors', 'people', 'going', 'through', 'interpersonal', 'traumatic', 'events', 'addition', 'collectivetrauma', 'covid', 'domestic', 'violence', 'gender', 'based', 'violence', 'abuse', 'mukhtar', '2020d', 'financial', 'burden', 'loneliness', 'emotional', 'behavioral', 'problems', 'grief', 'bereavement', 'losing', 'family', 'mental', 'health', 'issues', 'physical', 'injuries', 'fatalities', 'isolated', 'people', 'facing', 'psychological', 'issues', 'require', 'trauma', 'focused', 'psychologicalsupport', 'mental', 'health', 'psychological', 'support', 'guidance', 'treatment', 'interventionand', 'information', 'psychological', 'cyber', 'counseling', 'smartphone', 'formukhtar', '115instance', 'mental', 'health', 'hotlines', 'professional', 'seeking', 'behaviors', 'should', 'beencouraged', 'endorsed', 'related', 'barriers', 'stigma', 'marginalization', 'discrimination', 'shaming', 'phobias', 'should', 'discouraged', 'through', 'government', 'driven', 'programsand', 'evidence', 'based', 'treatment', 'models', 'emerging', 'pandemics', 'command', 'intellectual', 'incapacity', 'decision', 'making', 'adegree', 'irrationality', 'bombarded', 'conflicting', 'opinions', 'conspiracy', 'theories', 'empirical', 'interpretation', 'origins', 'impacts', 'result', 'moral', 'judgmentsbased', 'religious', 'cultural', 'beliefs', 'emergence', 'garnered', 'plethoraof', 'moral', 'judgments', 'orbiting', 'around', 'sexual', 'morality', 'substance', 'abuse', 'arrival', 'ofsars', 'blame', 'directed', 'towards', 'primitive', 'farming', 'practices', 'guangzhou', 'theunprecedented', 'covid', 'pandemic', 'welcomed', 'magnitude', 'misinfodemics', 'amultitude', 'fronts', 'ophir', 'richtel', 'psychological', 'predisposition', 'rejectauthorized', 'information', 'through', 'denial', 'byproduct', 'founded', 'mistrust', 'thetendency', 'major', 'social', 'political', 'events', 'through', 'conspiracies', 'partisan', 'motivations', 'byproduct', 'conspiracy', 'thinking', 'likely', 'explanatory', 'factors', 'understanding', 'believes', 'covid', 'misinfodemics', 'association', 'betweenconspiracy', 'thinking', 'defense', 'mechanism', 'denial', 'could', 'potential', 'reason', 'therefusal', 'corrective', 'actions', 'several', 'health', 'related', 'cases', 'carey', 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'state', 'global', 'response', 'populations', 'mostlikely', 'affected', 'emerging', 'disease', 'pandemic', 'simultaneous', 'misinfodemicsinclude', 'those', 'where', 'there', 'disproportionally', 'inadequate', 'health', 'literacy', 'disadvantaged', 'socioeconomic', 'groups', 'migrants', 'ethnic', 'minorities', 'vulnerable', 'groupsincluding', 'older', 'people', 'people', 'chronic', 'health', 'conditions', 'people', 'disability', 'mukhtar', '2020e', 'rowlands', 'politics', 'coronavirusconspiracy', 'theories', 'heavily', 'influenced', 'geopolitics', 'spread', 'regarding', 'origin', 'scale', 'prevention', 'treatment', 'diagnosis', 'disease', 'covid', 'being', 'viralbioweapon', 'genetically', 'engineered', 'rogue', 'government', 'racist', 'genocidalagenda', 'economic', 'psychological', 'chinese', 'biological', 'weaponconspiracy', 'theory', 'which', 'spread', 'throughout', 'united', 'states', 'united', 'kingdom', 'india', 'ukraine', 'biological', 'weapon', 'conspiracy', 'theory', 'spread', 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'feedback', 'manuscript', 'researchwas', 'partly', 'supported', 'grant', 'green', 'routes', '869764', 'availabilitythe', 'daily', 'aggregates', 'measurements', 'available', 'https', 'figshare', '14892642', 'tweet', 'available', 'https', 'github', 'echen102', 'covid', 'tweetids', 'other', 'datasetsand', 'visualizations', 'available', 'project', 'social', 'dynamics', 'epidemicpsychology', 'competing', 'intereststhe', 'author', 'declare', 'competing', 'interests', 'epidemic', 'psychology', 'works', 'twittersupplementary', 'materialsspatial', 'representativeness', 'twitter', 'geographical', 'penetration', 'twitterdata', 'reflects', 'spatial', 'distribution', 'population', 'compared', 'activity', 'state', 'withthe', 'census', 'population', 'figure', 'shows', 'number', 'twitter', 'users', 'volume', 'their', 'tweetscorrelate', 'strongly', 'census', 'population', 'population', 'estimates', 'level', 'states', 'https', 'census', '17log', 'population', '1213141516log', 'tweets', '08814', '17log', 'population', '91011121314log', 'users', '106figure', 'correlation', 'census', 'population', 'states', 'estimated', 'thenumber', 'tweets', 'posted', 'those', 'states', 'number', 'unique', 'twitter', 'users', 'postedat', 'least', 'tweet', 'right', 'during', 'period', 'analysis', 'linear', 'shown', 'together', 'thecoefficient', 'determination', 'linear', 'regression', 'pearson', 'correlation', 'coefficient', 'andthe', 'coefficient', 'number', 'tweets', 'twitter', 'users', 'scale', 'almost', 'linearlywith', 'population', 'estimates', 'signals', 'racism', 'twitter', 'discussions', 'corroborate', 'qualitative', 'intuition', 'first', 'phase', 'wascharacterized', 'discussions', 'depicted', 'pandemic', 'foreign', 'problem', 'often', 'using', 'racist', 'undertones', 'wemeasured', 'volume', 'tweets', 'containing', 'hashtags', 'clear', 'indicators', 'content', 'either', 'racist', 'oraimed', 'antagonizing', 'china', 'manually', 'selected', 'those', 'hashtags', 'hashtags', 'appeared', 'atleast', 'times', 'dataset', 'figure', 'shows', 'normalized', 'volume', 'those', 'hashtags', 'theypeak', 'during', 'first', 'phase', 'related', 'mentions', 'focus', 'twitter', 'discussions', 'china', 'asianforeigners', 'their', 'peaks', 'breakdown', 'phases', 'state', 'during', 'period', 'analysis', 'country', 'fragmented', 'regions', 'thatwere', 'dealing', 'different', 'stages', 'outbreak', 'policies', 'contrast', 'varied', 'across', 'states', 'assesswhether', 'those', 'differences', 'impacted', 'unfolding', 'psycho', 'social', 'epidemics', 'across', 'states', 'computed', 'themeasures', 'represent', 'relative', 'presence', 'three', 'phases', 'refusal', 'anger', 'acceptance', 'state', 'individually', 'results', 'shown', 'figure', 'despite', 'minor', 'differences', 'state', 'level', 'curvesindicate', 'three', 'phases', 'place', 'states', 'sequence', 'phases', 'consistent', 'across', 'states', 'refusal', 'followed', 'anger', 'which', 'followed', 'acceptance', 'there', 'little', 'variation', 'thetimes', 'transition', 'between', 'phases', 'epidemic', 'psychology', 'works', 'twitter']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model.show_topic(0, topn=words))#dict and encoding matrix values
dict(model.show_topic(0,topn=5))
{'covid': 0.3733564956223995,
'psycholog': 0.26785991090419725,
'social': 0.2517797817165566,
'epidem': 0.19223792506781598,
'health': 0.18194229083665533}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph14.png')
# LSA Model
number_of_topics = 20 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_psychology =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_psychology,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled17.png')
cosine-similarity
covid psycholog social epidem health
covid 1 0.150447 0.0375207 -0.0516623 0.0207802
psycholog 0.150447 1 0.168003 0.125162 0.380477
social 0.0375207 0.168003 1 0.44382 0.0839178
epidem -0.0516623 0.125162 0.44382 1 0.0126691
health 0.0207802 0.380477 0.0839178 0.0126691 1
def load_data(path,file_name):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
document = ''
i=0
with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for word in words:
i=i+1
document = document+word+' '
if i == 200:
documents_list.append(document)
document,i = '',0
if len(documents_list) == 2:
break
print("Total Number of Documents:",len(documents_list))
#titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count=load_data("","covid-science.txt")
Number of words in text file : 120257 Total Number of Documents: 2
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'medical', 'centers', 'hence', 'cases', 'images', 'become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'systems', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels', 'manuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november'] ['publication', 'november', 'current', 'version', 'december', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '18136', 'gómez', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'andalusian', 'research', 'institute', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'atlanttic', 'research', 'center']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science=dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'support': 0.3289947178827898,
'granada': 0.24814037209037257,
'covid': 0.22769581399154248,
'programm': 0.21932981192185977,
'research': 0.21932981192185977}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph15.png')
# LSA Model
number_of_topics = 2 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled18.png')
cosine-similarity
support granada covid programm research
support 1 0.956969 -0.0794645 1 1
granada 0.956969 1 0.213226 0.956969 0.956969
covid -0.0794645 0.213226 1 -0.0794645 -0.0794645
programm 1 0.956969 -0.0794645 1 1
research 1 0.956969 -0.0794645 1 1
document_list,titles,word_count=load_data("","covid-non-science.txt")
Number of words in text file : 8713 Total Number of Documents: 2
clean_text=preprocess_data(document_list)
['covid', 'recovery', 'covid', 'reality', 'reuters', 'published', 'updated', 'jones', 'london', 'reuters', 'world', 'shares', 'inched', 'towards', 'month', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'nagging', 'coronavirus', 'nerves', 'market', 'rally', 'fuelled', 'record', 'numbers', 'largely', 'blown', 'itself', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'decade', 'stimulus', 'ensured', 'optimism', 'remained', 'chinese', 'shares', 'charged', 'their', 'highest', 'level', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'traders', 'surprise', 'currency', 'commodity', 'markets', 'subdued', 'after', 'otherwise', 'strong', 'confidence', 'sensitive', 'stalwarts', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday', 'think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'doused', 'enthusiasm', 'societe', 'generale', 'strategist', 'jukes', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'places', 'return'] ['infection', 'rates', 'which', 'nervous', 'against', 'basket', 'currencies', 'dollar', 'slightly', 'early', 'london', 'trading', 'still', 'firmly', 'track', 'biggest', 'weekly', 'since', 'first', 'though', 'gained', 'against', 'swiss', 'franc', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown', 'futures', 'volumes', 'lower', 'usual', 'markets', 'holiday', 'friday', 'independence', 'nonfarm', 'payrolls', 'surged', 'million', 'above', 'average', 'forecast', 'million', 'thanks', 'rises', 'hospitality', 'sectors', 'economists', 'noted', 'there', 'caveats', 'upbeat', 'headline', 'figures', 'number', 'permanent', 'losers', 'continued', 'increasing', 'million', 'while', 'unemployment', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'analysis', 'unemployment', 'behind', 'developed', 'market', 'peers', 'barring', 'canada', 'recovery', 'faces', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'cases']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_non_science =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'market': 0.2986573559242644,
'covid': 0.21979867961934102,
'friday': 0.18596179123202222,
'infect': 0.15212490284470373,
'copper': 0.1465324530795617}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph16.png')
# LSA Model
number_of_topics = 2 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_non_science =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_non_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled19.png')
cosine-similarity
market covid friday infect year
market 1 0.741138 0.986861 0.813347 0.741138
covid 0.741138 1 0.839872 0.212223 1
friday 0.986861 0.839872 1 0.708661 0.839872
infect 0.813347 0.212223 0.708661 1 0.212223
year 0.741138 1 0.839872 0.212223 1
document_list,titles,word_count=load_data("","covid-psychology.txt")
Number of words in text file : 86550 Total Number of Documents: 2
clean_text=preprocess_data(document_list)
['international', 'sociology', 'author', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '0268580920948807', 'journals', 'sagepub', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid'] ['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'misinfodem': 0.5033743541941371,
'covid': 0.36297115033235144,
'articl': 0.21459251935197443,
'health': 0.14837863098037707,
'mechan': 0.14837863098037704}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph17.png')
# LSA Model
number_of_topics = 2 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_psychology =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_psychology,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled20.png')
cosine-similarity
misinfodem covid articl health mechan
misinfodem 1 0.976353 0.993795 0.755596 0.755596
covid 0.976353 1 0.946251 0.879335 0.879335
articl 0.993795 0.946251 1 0.678051 0.678051
health 0.755596 0.879335 0.678051 1 1
mechan 0.755596 0.879335 0.678051 1 1
def load_data(path,file_name):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for line in fin.readlines():
text = line.strip()
if text !='':
documents_list.append(text)
print("Total Number of Documents:",len(documents_list))
titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count=load_data("","covid-science.txt")
Number of words in text file : 120257 Total Number of Documents: 14379
clean_text=preprocess_data(document_list)
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['count'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['12230'] ['15535'] [] [] [] [] [] [] [] [] [] [] [] [] [] ['14291'] ['19916'] ['24897'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['12319'] ['15953'] ['18584'] [] [] [] [] [] [] [] [] [] [] ['11823'] ['19101'] ['29100'] ['44582'] ['62357'] ['77793'] [] ['denote', 'number', 'values', 'values'] ['standard', 'deviation', 'values', 'minimum', 'value'] ['maximum', 'value', 'first', 'quartile', 'second', 'quartile'] ['third', 'quartile', 'respectively', 'these', 'notations'] ['table', 'statistical', 'description'] ['considered', 'dataset', 'objective', 'analysis'] ['correlation', 'between', 'weeki', 'weeki5'] ['confirmed', 'cases', 'different', 'states', 'through'] ['analysis', 'observed', 'there', 'strong', 'correlation'] ['between', 'complete', 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'around', 'world', 'which'] ['provide', 'available', 'covid', 'sources', 'minor'] ['adjustments', 'toolset', 'accommodate', 'various', 'types'] ['published', 'country', 'various', 'formats'] ['scales', 'channels', 'publish', 'frequencies', 'importantly'] ['countries', 'provide', 'access', 'historical'] ['covid', 'automatically', 'build', 'historical', 'lections', 'support', 'research', 'repeatedly', 'certain', 'frequency'] ['covid', 'scraper', 'processes', 'effective', 'manner'] ['collecting', 'countries', 'world', 'within', 'single'] ['about', 'minutes', 'after', 'processing'] ['cleaning', 'fetched', 'unified', 'saved', 'database'] ['sharing', 'daily', 'quality', 'checking', 'product'] ['production', 'global', 'covid', 'github', 'repository'] ['maintained', 'since', 'march', 'addition', 'visual', 'ization', 'component', 'developed', 'covid', 'scraper'] ['publish', 'product', 'service', 'public'] ['access'] ['covid', 'scraper', 'utilized', 'scraping', 'technolo', 'science', 'related', 'fields'] ['integrating', 'source', 'packages', 'tools'] ['extracting', 'network', 'simulation', 'image', 'parsing', 'automation', 'covid', 'scraper', 'highly', 'flexible'] ['automatic', 'toolset', 'process', 'tasks', 'unsupervised', 'under'] ['users', 'settings', 'nature', 'source', 'users'] ['easily', 'customize', 'sources', 'structure'] ['output', 'product', 'execution', 'logic', 'processing', 'frequency'] ['exception', 'handling', 'addition', 'users', 'modify'] ['source', 'extend', 'collecting', 'datasets', 'other'] ['purposes', 'support', 'wider', 'studies', 'tasks', 'gency', 'response', 'natural', 'disaster', 'detection', 'saving'] ['lives'] ['currently', 'limitation', 'quality', 'control'] ['validation', 'cannot', 'fully', 'automated', 'because', 'accuracy'] ['parsing', 'extracting', 'cannot', 'always', 'guaranteed'] ['using', 'current', 'packages', 'hence', 'users', 'intervene'] ['quality', 'control', 'process', 'image'] ['product', 'quality'] ['rapid', 'development', 'parsing', 'images'] ['updating', 'component', 'minimize', 'human', 'intervention'] ['automation', 'process']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'covid': 0.8353615686846244,
'imag': 0.35520141247889436,
'detect': 0.142179572403319,
'model': 0.12671521164640343,
'dataset': 0.10935919846834341}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph18.png')
# LSA Model
number_of_topics = 20 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled21.png')
cosine-similarity
covid imag detect model dataset
covid 1 -0.00267804 0.0046269 -0.001142 -0.00296758
imag -0.00267804 1 -0.00922806 -0.000851408 -0.00583234
detect 0.0046269 -0.00922806 1 0.00541258 -0.0051734
model -0.001142 -0.000851408 0.00541258 1 -0.000505639
dataset -0.00296758 -0.00583234 -0.0051734 -0.000505639 1
document_list,titles,word_count=load_data("","covid-non-science.txt")
Number of words in text file : 8713 Total Number of Documents: 387
clean_text=preprocess_data(document_list)
['covid', 'recovery', 'covid', 'reality'] ['reuters'] ['published', 'updated'] ['jones'] ['london', 'reuters', 'world', 'shares', 'inched', 'towards', 'month', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'nagging', 'coronavirus', 'nerves'] ['market', 'rally', 'fuelled', 'record', 'numbers', 'largely', 'blown', 'itself', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'decade', 'stimulus', 'ensured', 'optimism', 'remained'] ['chinese', 'shares', 'charged', 'their', 'highest', 'level', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'traders', 'surprise'] ['currency', 'commodity', 'markets', 'subdued', 'after', 'otherwise', 'strong', 'confidence', 'sensitive', 'stalwarts', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday'] ['think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'doused', 'enthusiasm', 'societe', 'generale', 'strategist', 'jukes'] ['three', 'elements', 'vaccine', 'hopes', 'decent', 'places', 'return', 'infection', 'rates', 'which', 'nervous'] ['against', 'basket', 'currencies', 'dollar', 'slightly', 'early', 'london', 'trading', 'still', 'firmly', 'track', 'biggest', 'weekly', 'since', 'first'] ['though', 'gained', 'against', 'swiss', 'franc', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown'] ['futures', 'volumes', 'lower', 'usual', 'markets', 'holiday', 'friday', 'independence'] ['nonfarm', 'payrolls', 'surged', 'million', 'above', 'average', 'forecast', 'million', 'thanks', 'rises', 'hospitality', 'sectors'] ['economists', 'noted', 'there', 'caveats', 'upbeat', 'headline', 'figures'] ['number', 'permanent', 'losers', 'continued', 'increasing', 'million', 'while', 'unemployment', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'analysis', 'unemployment', 'behind', 'developed', 'market', 'peers', 'barring', 'canada'] ['recovery', 'faces', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'cases', 'reverse', 'plans', 'stores', 'reopen', 'activities', 'resume'] ['three', 'dozen', 'states', 'increases', 'covid', 'cases', 'cases', 'florida', 'spiking', 'above'] ['nevertheless', 'markets', 'largely', 'overlooking', 'spikes', 'taking', 'overall', 'situation', 'still', 'improving', 'overall'] ['german', 'government', 'yields', 'basis', 'points', 'their', 'biggest', 'weekly', 'month', 'though', 'nudged', 'friday', 'riskier', 'italian', 'yields', 'though', 'which', 'their', 'lowest', 'since', 'march'] ['prices', 'eased', 'after', 'otherwise', 'solid', 'brent', 'crude', 'barrel', 'while', 'crude', 'dropped', 'barrel', 'around', 'months'] ['copper', 'prices', 'poised', 'seventh', 'consecutive', 'weekly', 'their', 'longest', 'winning', 'streak', 'nearly', 'three', 'years', 'despite', 'slight', 'easing', 'after', 'supplier', 'chile', 'assured', 'traders', 'about', 'supply'] ['three', 'month', 'copper', 'hovering', 'tonne', 'ploughed', 'march'] ['issue', 'hangs', 'markets', 'surge', 'secondary', 'infections', 'trigger', 'second', 'national', 'rather', 'regional', 'shutdowns', 'malcolm', 'freeman', 'director', 'kingdom', 'futures', 'wrote'] ['reporting', 'jones'] ['newspaper'] ['covid', 'vaccine', 'frontrunners'] ['reuters'] ['published', 'october', 'updated', 'october'] ['reuters', 'pfizer', 'could', 'november', 'authorization', 'covid', 'vaccine', 'developing', 'opening', 'possibility', 'vaccine', 'being', 'available', 'united', 'states', 'moderna', 'astrazeneca', 'close', 'behind', 'largest', 'drugmaker', 'likely', 'early', 'their', 'vaccine', 'candidates', 'before', 'potential', 'vaccines', 'being', 'developed', 'tested', 'globally', 'covid', 'pandemic', 'human', 'trials', 'according', 'world', 'health', 'organization', 'following', 'tracks', 'candidates', 'final', 'stages', 'testing', 'separate', 'factbox', 'coronavirus', 'vaccines', 'under', 'development', 'click', 'company', 'stage', 'development', 'expected', 'doses', 'pfizer', 'stage', 'trials', 'underway', 'option', 'biontech', 'pfizer', 'earlier', 'october', 'could', 'emergency', 'authorization', 'november', 'indicating', 'vaccine', 'could', 'potentially', 'available', 'additional', 'canada', 'specified', 'japan', 'moderna', 'completed', 'enrollment', 'option', 'subjects', 'stage', 'trial', 'additional', 'interim', 'results', 'trial', 'expected', 'november', 'japan', 'distributed', 'takeda', 'canada', 'specified', 'johnson', 'johnson', 'large', 'vaccine', 'trial', 'resume', 'after', 'paused', 'under', 'subsequent', 'agreement', 'illness', 'study', 'volunteer', 'results', 'trial', 'expected', 'option', 'early', 'additional', 'canada', 'japan', 'applicable', 'astrazeneca', 'stage', 'trials', 'underway', 'supply', 'deals', 'countries', 'oxford', 'university', 'trial', 'vaccine', 'candidate', 'doses', 'pause', 'since', 'after', 'patient', 'trial', 'results', 'expected', 'coming', 'weeks', 'commitment', 'deliver', 'total', 'least', 'additional', 'italy', 'germany', 'netherlands', 'france', 'additional', 'canada', 'japan', 'sinovac', 'biotech', 'stage', 'trials', 'brazil', 'vaccine', 'approved', 'emergency', 'indonesia', 'turkey', 'underway', 'china', 'supply', 'prelim', 'expected', 'november', 'indonesia', 'least', 'doses', 'before', 'gamaleya', 'research', 'stage', 'trial', 'covid', 'people', 'inoculated', 'institute', 'vaccine', 'sputnik', 'underway', 'russia', 'supply', 'deals', 'since', 'prelim', 'countries', 'including', 'india', 'expected', 'november', 'brazil', 'saudi', 'arabia', 'doses', 'cansino', 'biologics', 'vaccine', 'candidate', 'final', 'stage', 'mexico', 'doses', 'early', 'trials', 'approved', 'chinese', 'military', 'stage', 'trial', 'underway', 'pakistan', 'sinopharm', 'group', 'stage', 'trials', 'expects', 'produce', 'underway', 'billion', 'doses', 'reporting', 'dania', 'nadeem', 'mrinalika', 'bengaluru', 'editing', 'ankur', 'banerjee', 'samuel'] ['newspaper'] ['giants', 'another', 'covid'] ['reuters'] ['published', 'november', 'updated', 'november'] ['giants', 'another', 'covid'] ['monday', 'night', 'notified', 'player', 'tested', 'positive', 'covid', 'giants', 'announced', 'tuesday', 'morning', 'player', 'immediately', 'isolated', 'contact', 'tracing', 'process', 'initiated', 'close', 'contacts', 'staff', 'members', 'identified', 'informed', 'remain', 'today'] ['giants', 'entering', 'their', 'second', 'player', 'positive', 'covid'] ['prior', 'playing', 'tampa', 'buccaneers', 'offensive', 'lineman', 'hernandez', 'tested', 'positive'] ['hernandez', 'spent', 'nearly', 'weeks', 'reserve', 'covid', 'returned', 'played', 'sunday', 'victory', 'philadelphia', 'eagles'] ['field', 'level', 'media'] ['newspaper'] ['covid', 'antivirals', 'taskforce', 'launched'] ['media'] ['published', 'april', 'updated', 'april'] ['covid', 'treatments', 'being', 'sought', 'government', 'experts', 'speed', 'recovery', 'people', 'become', 'infected', 'virus'] ['government', 'taskforce', 'supercharge', 'search', 'antiviral', 'medications', 'covid'] ['prime', 'minister', 'boris', 'johnson', 'group', 'medicines', 'covid', 'tracks'] ['hoped', 'antivirals', 'could', 'reduce', 'infections', 'limit', 'impact', 'variants'] ['protect', 'people', 'cannot', 'vaccines', 'those', 'fully', 'protected', 'after', 'having'] ['officials', 'bring', 'treatments', 'online'] ['could', 'people', 'covid', 'those', 'close', 'contact', 'could', 'offered', 'antiviral', 'medication', 'offered', 'tablet', 'infection', 'spreading', 'speed', 'recovery'] ['antivirals', 'taskforce', 'which', 'appoint', 'chair', 'search', 'promising', 'potential', 'antiviral', 'medicines', 'which', 'taken'] ['taskforce', 'support', 'development', 'drugs', 'through', 'clinical', 'trials', 'manufacturing', 'opportunities'] ['antiviral', 'drugs', 'medication', 'specifically', 'treating', 'viral', 'infections', 'killing', 'preventing', 'growth', 'viruses'] ['success', 'vaccination', 'programme', 'demonstrated', 'achieve', 'bring', 'together', 'brightest', 'minds', 'johnson'] ['antivirals', 'taskforce', 'develop', 'innovative', 'treatments', 'covid', 'tracks'] ['these', 'could', 'provide', 'another', 'vital', 'defence', 'against', 'future', 'increase', 'infections', 'lives'] ['health', 'secretary', 'hancock', 'medicines', 'vital', 'weapon', 'protect', 'loved', 'terrible', 'virus'] ['modelled', 'success', 'vaccines', 'therapeutics', 'taskforces', 'which', 'played', 'crucial', 'response', 'pandemic', 'bringing', 'together', 'supercharge', 'search', 'antiviral', 'treatments', 'autumn'] ['government', 'chief', 'scientific', 'adviser', 'patrick', 'vallance', 'speed', 'which', 'vaccines', 'therapeutics', 'dexamethasone', 'identified', 'deployed', 'against', 'covid', 'critical', 'pandemic', 'response'] ['antivirals', 'tablet', 'another', 'response'] ['could', 'protect', 'those', 'protected', 'ineligible', 'vaccines'] ['could', 'another', 'layer', 'defence', 'variants', 'concern'] ['nikita', 'kanani', 'medical', 'director', 'primary', 'england', 'research', 'reality', 'record', 'speed', 'during', 'pandemic', 'taskforce', 'identify', 'convenient', 'treatments', 'patients', 'covid'] ['commenting', 'announcement', 'penny', 'visiting', 'professor', 'pharmaceutical', 'medicine', 'kings', 'college', 'london', 'antiviral', 'treatment', 'influenza', 'shown', 'reduce', 'hospitalisation', 'prevent', 'death', 'epidemic', 'pandemic', 'waves', 'similar', 'preferably', 'simple', 'antiviral', 'suitable', 'community', 'range', 'interventions', 'critical', 'enable', 'remain', 'covid', 'pandemic', 'event', 'emergence', 'viral', 'variants', 'insensitive', 'vaccines'] ['there', 'number', 'early', 'phase', 'trials', 'looking', 'antiviral', 'medications'] ['newspaper'] ['african', 'covid', 'deaths'] [] ['published', 'february', 'updated', 'february'] ['africa', 'thursday', 'recorded', 'deaths', 'covid', 'milestone', 'likely', 'understate', 'continent', 'billion', 'people', 'battles', 'second', 'infections'] ['countries', 'region', 'death', 'reported', 'cases', 'according', 'tally'] ['continent', 'relatively', 'spared', 'pandemic', 'except', 'oceania', 'reach', 'threshold', 'deaths', 'which', 'europe', 'crossed', 'april'] ['south', 'africa', 'worst', 'african', 'country', 'rolled', 'testing', 'campaign', 'start', 'pandemic'] ['country', 'recorded', 'nearly', 'million', 'cases', 'deaths'] ['those', 'figures', 'based', 'daily', 'reports', 'communicated', 'health', 'authorities', 'reflect', 'fraction', 'actual', 'health', 'specialists'] ['cases', 'clearly', 'under', 'reported', 'because', 'access', 'healthcare', 'facilities', 'under', 'reporting', 'milder', 'cases', 'south', 'african', 'virologist', 'barry', 'schoub', 'member', 'scientific', 'council', 'south', 'african', 'ministry', 'health'] ['understaffed', 'health', 'facilities', 'means', 'meant', 'african', 'countries', 'unable', 'testing'] ['countries', 'mainly', 'tests', 'capitals', 'further', 'moves', 'urban', 'centres', 'there', 'tests', 'explained', 'french', 'epidemiologist', 'emmanuel', 'baron', 'doctors', 'without', 'borders'] ['disease', 'unnoticed', 'asymptomatic', 'patients', 'symptoms', 'confused', 'others', 'added'] ['covid', 'found', 'pawpaw'] ['zimbabwe', 'country', 'devastated', 'economy', 'mismanaged', 'health', 'system', 'hospitals', 'filled', 'covid', 'patients', 'exhausted', 'doctors', 'overwhelmed', 'nurses', 'official', 'number', 'cases', 'remains'] ['tanzania', 'stopped', 'testing', 'after', 'claiming', 'found', 'positive', 'covid', 'pawpaw', 'quail', 'tanzanian', 'government', 'released', 'official', 'figures', 'april'] ['someone', 'continent', 'would', 'deaths', 'infection', 'probably', 'would', 'believed', 'nkengasong', 'africa', 'director', 'centers', 'disease', 'control', 'preventions', 'reporters', 'thursday'] ['africa', 'however', 'significantly', 'lower', 'europe', 'world', 'affected', 'region', 'deaths', 'recorded', 'other', 'regions', 'badly', 'latin', 'america', 'deaths', 'united', 'states', 'canada', 'deaths'] ['after', 'sharp', 'increase', 'january', 'africa', 'figures', 'fallen', 'sharply', 'weeks', 'seven', 'continent', 'recorded', 'deaths', 'percent', 'previous'] ['height', 'pandemic', 'january', 'continent', 'deaths'] ['disaster'] ['while', 'coronavirus', 'figures', 'clearly', 'underestimated', 'health', 'disaster', 'africa', 'baron'] ['several', 'studies', 'antibodies', 'which', 'possible', 'detect', 'whether', 'person', 'recovered', 'previously', 'exposed', 'virus', 'underway', 'african', 'countries', 'should', 'provide', 'better', 'impact', 'pandemic', 'region'] ['south', 'africa', 'where', 'almost', 'latest', 'cases', 'attributed', 'variant', 'virus', 'known', 'contagious', 'which', 'spread', 'widely', 'represents', 'nearly', 'deaths', 'reported', 'cases', 'continent'] ['other', 'african', 'countries', 'affected', 'egypt', 'deaths', 'cases', 'morocco', 'deaths', 'cases'] ['south', 'africa', 'country', 'covid', 'fatalities', 'continent', 'counting', 'deaths', 'inhabitants', 'ahead', 'tunisia', 'deaths', 'eswatini'] ['lagging', 'behind', 'vaccination', 'continent', 'leading', 'industrial', 'powerhouse', 'administered', 'first', 'vaccines', 'wednesday'] ['globally', 'covid', 'caused', 'million', 'infections', 'million', 'deaths', 'since', 'start', 'epidemic', 'wuhan', 'china', 'december'] [] ['newspaper'] ['covid', 'hotspots', 'industry'] ['australian', 'associated', 'press'] ['published', 'august', 'updated', 'august'] ['fitness', 'industry', 'perception', 'covid', 'hotspots', 'saying', 'there', 'transmission', 'virus', 'linked'] ['fitness', 'australia', 'chief', 'executive', 'barrie', 'elvish', 'fitness', 'australia', 'gathered', 'random', 'sample', 'operators', 'proves'] ['since', 'reopened', 'there', 'million', 'visits', 'recorded', 'community', 'transmission'] ['cannot', 'restaurants', 'where', 'cluster', 'cases', 'started', 'spread', 'however', 'still', 'being', 'categorised', 'these', 'other', 'entertainment', 'venues', 'negative', 'light', 'statement', 'thursday'] ['however', 'misinformation', 'negative', 'commentary', 'portrayed', 'health', 'fitness', 'facilities', 'hotspots', 'covid', 'resulting', 'people', 'being', 'scared', 'exercise', 'their', 'despite', 'additional', 'precautions', 'stringent', 'hygiene', 'practices'] ['fitness', 'australia', 'gathered', 'electronic', 'swipes', 'members', 'access', 'which', 'could', 'sophisticated', 'contact', 'tracing', 'should', 'required'] ['elvish', 'there', 'reported', 'cases', 'where', 'member', 'visited', 'while', 'infected', 'covid', 'health', 'authorities', 'identified', 'cases', 'transmission'] ['problem', 'actually', 'solution', 'getting', 'australians', 'active', 'often', 'prevent', 'lifestyle', 'related', 'disease', 'elvish'] ['urging', 'government', 'fitness', 'australia', 'industry', 'ensure', 'health', 'safety', 'community'] ['newspaper'] ['covid', 'victims', 'maintain', 'immunity'] ['australian', 'associated', 'press'] ['published', 'november', 'updated', 'november'] ['australian', 'researchers', 'discovered', 'patients', 'infected', 'covid', 'retain', 'immunity', 'against', 'virus', 'disease', 'least', 'eight', 'months'] ['research', 'strongest', 'evidence', 'vaccines', 'against', 'virus', 'periods'] ['previous', 'studies', 'found', 'first', 'antibodies', 'produced', 'human', 'after', 'infection', 'waned', 'after', 'first', 'months', 'raising', 'concerns', 'people', 'could', 'quickly', 'immunity'] ['research', 'allays', 'those', 'concerns'] ['study', 'result', 'collaboration', 'associate', 'professor', 'menno', 'monash', 'university', 'published', 'monday', 'preprint', 'server', 'medrxiv'] ['researchers', 'found', 'specific', 'within', 'human', 'immune', 'system', 'memory', 'remembers', 'infection', 'virus', 'challenged', 'again', 'through', 'exposure', 'virus', 'triggers', 'protective', 'immune', 'response', 'through', 'rapid', 'production', 'protective', 'antibodies'] ['researchers', 'recruited', 'covid', 'patients', 'blood', 'samples', 'between', 'infection', 'infection'] ['other', 'studies', 'looking', 'antibody', 'response', 'researchers', 'found', 'antibodies', 'against', 'virus', 'started', 'after', 'infection'] ['however', 'patients', 'continued', 'memory', 'cells', 'recognised', 'components', 'virus', 'spike', 'nucleocapsid', 'proteins'] ['these', 'virus', 'specific', 'memory', 'cells', 'present', 'eight', 'months', 'after', 'infection'] ['associate', 'professor', 'results', 'efficacy', 'vaccine', 'against', 'virus', 'explained', 'there', 'examples', 'genuine', 'reinfection', 'millions', 'people', 'tested', 'positive', 'virus', 'globally'] ['these', 'results', 'important', 'because', 'definitively', 'patients', 'infected', 'covid', 'virus', 'retain', 'immunity', 'against', 'virus', 'disease'] ['black', 'cloud', 'hanging', 'potential', 'protection', 'could', 'provided', 'covid', 'vaccine', 'gives', 'vaccine', 'vaccines', 'developed', 'provide', 'protection'] ['newspaper'] ['origins', 'covid'] ['reuters'] ['published', 'january', 'updated', 'january'] ['shanghai', 'reuters', 'world', 'health', 'organization', 'works', 'china', 'investigate', 'origins', 'covid', 'following', 'factbox', 'looks', 'about', 'pandemic', 'began'] ['china', 'origins'] ['coronavirus', 'causes', 'covid', 'known', 'first', 'identified', 'central', 'chinese', 'wuhan', 'january', 'scientists', 'believe', 'still', 'likely', 'originated', 'china'] ['peter', 'embarek', 'expert', 'safety', 'zoonotic', 'diseases', 'wuhan', 'offered', 'first', 'solid', 'clues', 'about', 'transmission', 'covid', 'investigation', 'would', 'start', 'there'] ['though', 'studies', 'suggested', 'covid', 'present', 'italy', 'spain', 'earlier', 'might', 'responsible', 'spike', 'pneumonia', 'cases', 'france', 'researchers', 'believe', 'could', 'entered', 'wuhan', 'europe'] ['closest', 'relative', 'nature', 'ratg13', 'virus', 'which', 'discovered', 'horseshoe', 'southwest', 'china', 'yunnan', 'province', 'genetic', 'match', 'between', 'makes', 'highly', 'likely', 'covid', 'originated', 'colonies', 'china', 'southwest', 'border', 'regions'] ['huanan', 'market'] ['initial', 'cluster', 'infections', 'traced', 'huanan', 'seafood', 'market', 'wuhan', 'leading', 'assume', 'patient', 'probably', 'trader', 'exposed', 'contaminated', 'products'] ['version', 'events', 'simplistic', 'explain', 'patterns', 'infection', 'wuhan', 'elsewhere'] ['earliest', 'reported', 'cases', 'connection', 'market', 'wuhan', 'residents', 'hospitalised', 'turned', 'covid', 'huanan'] ['chinese', 'researchers', 'there', 'types', 'circulating', 'wuhan', 'which', 'associated', 'market'] ['scientists', 'virus', 'unusually', 'adapted', 'rapid', 'human', 'transmission', 'making', 'unlikely', 'first', 'human', 'contact', 'seafood', 'market'] ['intermediary', 'species'] ['investigators', 'intermediary', 'species', 'allowed', 'original', 'humans'] ['preliminary', 'scientific', 'papers', 'china', 'identified', 'snakes', 'potential', 'candidates', 'similar', 'coronavirus', 'infections', 'found', 'pangolins', 'illegally', 'trafficked', 'china'] ['scholars', 'believe', 'there', 'intermediary', 'species', 'virus', 'transmitted', 'directly', 'humans', 'possibly', 'multiple', 'occasions'] ['first', 'people', 'infected', 'likely', 'traders', 'droppings', 'traditional', 'chinese', 'medicine', 'could', 'carried', 'huanan', 'seafood', 'market', 'causing', 'superspreader', 'event', 'allowed', 'pandemic', 'begin'] ['function'] ['though', 'there', 'credible', 'supporting', 'evidence', 'researchers', 'still', 'possibility', 'virus', 'released', 'accidentally', 'specialist', 'wuhan', 'institute', 'virology'] ['there', 'indication', 'contains', 'synthetic', 'insertions', 'researchers', 'could', 'subject', 'process', 'known', 'function', 'where', 'forces', 'virus', 'become', 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'overseas', 'within', 'tested'] ['workers', 'vulnerable', 'setting', 'including', 'healthcare', 'military', 'education', 'corrections', 'tested'] ['people', 'brisbane', 'coast', 'cairns', 'first', 'nations', 'community', 'tested', 'symptoms'] ['people', 'travelling', 'declared', 'covid', 'hotspot', 'interstate', 'tested'] [] ['south', 'australia'] ['residents', 'advised', 'tested', 'travelled', 'overseas', 'interstate'] ['testing', 'advised', 'people', 'contact', 'confirmed', 'displaying', 'coronavirus', 'symptoms'] ['healthcare', 'workers', 'direct', 'patient', 'contact', 'residential', 'workers', 'fever', 'acute', 'respiratory', 'infection', 'shortness', 'breath', 'cough', 'throat', 'urged', 'tested'] ['people', 'tanunda', 'nuriootpa', 'williamstown', 'angaston', 'lyndoch', 'march', 'developed', 'covid', 'symptoms', 'should', 'immediately', 'isolate', 'tested', 'after', 'cases', 'linked', 'barossa', 'region'] ['health', 'recommends', 'testing', 'qantas', 'staff', 'affected', 'areas', 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'through', 'public', 'health', 'services', 'tested'] ['people', 'cruise', 'contact', 'person', 'covid', 'apply', 'tested'] ['state', 'government', 'changed', 'testing', 'criteria', 'allow', 'testing', 'people', 'spent', 'tasmania', 'northwest', 'developed', 'fever', 'respiratory', 'symptoms'] ['northern', 'territory', 'people', 'returned', 'interstate', 'overseas', 'develop', 'respiratory', 'illness', 'without', 'fever', 'tested'] ['people', 'close', 'contact', 'confirmed', 'covid', 'develop', 'respiratory', 'illness', 'without', 'fever', 'tested'] ['people', 'severe', 'community', 'acquired', 'pneumonia', 'there', 'clear', 'cause', 'testing'] ['healthcare', 'workers', 'directly', 'patients', 'frontline', 'workers', 'including', 'police', 'emergency', 'workers', 'educators', 'retail', 'pharmacists', 'disability', 'workers', 'display', 'respiratory', 'illness', 'fever', 'tested'] [] ['people', 'recently', 'travelled', 'overseas', 'cruise', 'contact', 'confirmed', 'developed', 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'record', 'keeping'] ['classes', 'limited', 'person', 'square', 'metres', 'allowed', 'elsewhere', 'staffed', 'staffed', 'limit', 'person', 'eight', 'square', 'metres', 'applies'] ['number', 'people', 'allowed', 'attend', 'weddings', 'funerals', 'indoor', 'religious', 'gatherings', 'scrapped', 'density', 'limit', 'person', 'square', 'metres', 'applies'] ['rules', 'apply', 'libraries', 'arcades', 'homes', 'outdoor', 'seated', 'entertainment', 'venues'] ['seated', 'entertainment', 'venues', 'cinemas', 'permitted', 'capacity', 'maximum', 'people'] ['indoor', 'seated', 'entertainment', 'venues', 'galleries', 'allowed', 'reach', 'capacity', 'people', 'density', 'limit', 'person', 'square', 'metres', 'applies'] ['amusement', 'parks', 'venue', 'capacity', 'density', 'limit', 'person', 'square', 'metres', 'applies', 'indoors', 'square', 'metres', 'outdoors'] ['gaming', 'allowed', 'limit', 'person', 'square', 'metres', 'enforced', 'every', 'second', 'machine', 'turned'] ['person', 'square', 'metres', 'permitted', 'inside', 'brothels', 'strip', 'clubs', 'while', 'patrons', 'strip', 'clubs', 'scrapped', 'brothels', 'allowed', 'people'] ['newspaper'] ['covid', 'restrictions', 'across', 'europe'] ['reuters'] ['published', 'february', 'updated', 'february'] ['reuters', 'britain', 'whose', 'economy', 'among', 'hardest', 'europe', 'covid', 'pandemic', 'monday', 'announce', 'phased', 'roadmap', 'lockdown', 'aided', 'world', 'fastest', 'vaccine', 'rollouts'] ['announcement', 'parliament', 'expected', 'confirm', 'opening', 'schools', 'march', 'stipulate', 'gradual', 'easing', 'other', 'restrictions', 'notably', 'retailers', 'hospitality', 'venues'] ['following', 'snapshot', 'restrictions', 'place', 'europe', 'other', 'leading', 'economies'] ['germany', 'essential', 'stores', 'closed', 'until', 'least', 'march', 'restaurants', 'offer', 'meals', 'museums', 'cinemas', 'closed', 'hotels', 'business', 'travellers', 'schools', 'federal', 'states', 'partially', 'opening', 'companies', 'offer', 'staff', 'option', 'where', 'possible'] ['france', 'nationwide', 'curfew', 'place', 'between', 'hours', 'schools', 'shops', 'cafes', 'restaurants', 'along', 'theatres', 'cinemas', 'museums', 'galleries', 'opening', 'anyone', 'entering', 'france', 'produce', 'negative', 'covid', 'entering', 'france', 'outside', 'european', 'union', 'allowed', 'except', 'urgent', 'reasons', 'italy', 'country', 'divided', 'orange', 'yellow', 'white', 'zones', 'restrictions', 'accordingly', 'present', 'seven', 'regions', 'provinces', 'orange', 'yellow', 'orange', 'means', 'restaurants', 'closed', 'people', 'cannot', 'leave', 'their', 'towns', 'residence', 'except', 'emergencies', 'schools', 'closed', 'these', 'regions', 'least', 'partly', 'yellow', 'zones', 'theatres', 'cinemas', 'closed', 'nationwide', 'travel', 'between', 'regions', 'subject', 'limits', 'spain', 'restrictions', 'madrid', 'taking', 'relaxed', 'approach', 'allowing', 'customers', 'drink', 'inside', 'restaurants', 'until', 'other', 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'degree', 'normality', 'winter', 'fauci', 'state', 'union'] ['damage', 'pandemic'] ['biden', 'program', 'deliver', 'vaccines', 'people', 'complicated', 'already', 'intense', 'challenges', 'manufacturing', 'quantities', 'speed'] ['there', 'never', 'logistical', 'challenge', 'consequential', 'trying', 'getting'] ['according', 'biden', 'administering', 'million', 'shots', 'total', 'million', 'first', 'presidency', 'track', 'easily', 'surpassed', 'current', 'average', 'million', 'vaccinations'] ['death', 'biden', 'predictions', 'crisis', 'curbed'] ['however', 'million', 'doses', 'enough', 'provide', 'regimen', 'country', 'expected', 'ready'] ['million', 'people', 'received', 'least', 'vaccine', 'united', 'states', 'million', 'getting', 'doses'] ['freezing', 'weather', 'storms', 'united', 'states', 'slowed', 'nation', 'vaccine', 'efforts', 'million', 'doses', 'delayed'] ['gotten', 'million', 'doses', 'project', 'middle', 'caught', 'fauci', 'press', 'calling', 'temporary', 'setback'] 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'party', 'dance', 'floor', 'where', 'previously', 'bride', 'groom', 'permitted'] ['schools'] ['interschool', 'activities', 'resume', 'saturday', 'covid', 'safety'] ['school', 'sport', 'return', 'normal', 'parents', 'school', 'sites'] ['playing', 'instruments', 'singing', 'chanting', 'permitted', 'strict', 'restrictions'] ['formals', 'graduations', 'permitted'] ['school', 'formals', 'permitted', 'after'] ['kindergarten', 'orientation'] ['transition'] ['excursions', 'camps', 'resume', 'strict', 'guidelines'] ['school', 'choirs', 'musical', 'ensembles', 'resume', 'players', 'metre', 'distance', 'another', 'three', 'metres', 'players', 'reeded', 'woodwind', 'instruments'] ['community', 'languages', 'schools', 'program', 'resume', 'learning'] ['sport'] ['parent', 'attend', 'community', 'sporting', 'activities', 'physical', 'distancing', 'least', 'metres', 'maintained'] ['accommodation', 'facilities', 'overnight', 'event', 'organisers', 'implement', 'covid', 'safety'] ['carpooling', 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'officially', 'launched', 'kiwis', 'almost', 'population', 'downloaded'] ['wednesday', 'director', 'general', 'health', 'ashley', 'bloomfield', 'announced', 'there', 'cases', 'covid', 'zealand', 'fourth'] ['there', 'cases', 'overall', 'fortnight'] ['decreasing', 'numbers', 'nightclubs', 'first', 'since', 'march', 'thursday', 'provided', 'customers', 'seated', 'groups', 'under', 'receive', 'table', 'service'] ['ardern', 'further', 'review', 'gathering', 'numbers', 'currently', 'except', 'funerals', 'which', 'monday', 'cabinet', 'meeting'] ['regularly', 'adjust', 'settings', 'allow', 'activity', 'ardern'] ['showing', 'virus', 'squashed', 'months', 'zealander', 'being', 'treated', 'hospital', 'covid', 'require', 'intensive'] ['newspaper'] ['records', 'covid', 'cases', 'deaths'] ['reuters'] ['published', 'updated'] ['london', 'reuters', 'britain', 'reported', 'daily', 'covid', 'cases', 'government', 'figures', 'showed', 'thursday', 'slightly', 'reported', 'before', 'which', 'highest', 'daily', 'total', 'since', 'february'] ['britain', 'reported', 'seven', 'further', 'deaths', 'within', 'positive', 'covid', 'wednesday', 'taking', 'total', 'death', 'measure'] ['british', 'adult', 'population', 'received', 'doses', 'covid', 'vaccine', 'received', 'least', 'reporting', 'david', 'milliken', 'editing', 'holton'] ['newspaper'] ['drugs', 'wrongly', 'touted', 'covid', 'treatments'] ['australian', 'associated', 'press'] ['published', 'august', 'updated', 'august'] ['medicines', 'falsely', 'touted', 'covid', 'treatments'] ['hydroxychloroquine', 'treat', 'malaria', 'rheumatoid', 'arthritis', 'lupus', 'there', 'reliable', 'evidence', 'prevent', 'treat', 'covid', 'studies', 'found', 'things', 'worse'] ['remdesivir', 'antiviral', 'medicine', 'people', 'hospital', 'severe', 'covid', 'recover', 'faster', 'people', 'milder', 'covid'] ['dexamethasone', 'reduces', 'inflammation', 'treat', 'asthma', 'arthritis', 'reduces', 'dying', 'people', 'hospital', 'serious', 'covid', 'breathe', 'doesn', 'people', 'severe', 'covid', 'could', 'things', 'worse'] ['ephedra', 'vitamin', 'other', 'complementary', 'medicines', 'found', 'effective', 'against', 'covid', 'outside', 'intensive'] ['source', 'medicinewise'] ['newspaper'] ['covid', 'schools', 'study', 'breakdown'] ['australian', 'associated', 'press'] ['published', 'april', 'updated', 'april'] ['national', 'centre', 'immunisation', 'research', 'surveillance', 'ncirs', 'study', 'covid', 'schools', 'found'] ['total', 'students', 'staff', 'members', 'schools', 'march', 'april', 'diagnosed', 'covid'] ['people', 'students', 'staff', 'members'] ['those', 'people', 'total', 'close', 'contacts', 'within', 'school', 'period'] ['close', 'contacts', 'people', 'caught', 'covid'] ['those', 'people', 'students', 'disease', 'others'] ['students', 'passed', 'coronavirus', 'school', 'staff', 'members'] ['spread', 'covid', 'within', 'schools', 'limited', 'virus', 'transmission', 'children', 'schools', 'influenza'] ['newspaper'] ['sydney', 'repeat', 'covid', 'breaches'] ['australian', 'associated', 'press'] ['published', 'october', 'updated', 'october'] ['sydney', 'portugal', 'community', 'being', 'forced', 'seven', 'after', 'failing', 'comply', 'covid', 'regulations', 'after', 'three', 'warnings'] ['liquor', 'gaming', 'director', 'compliance', 'dimitri', 'argeres', 'portugal', 'marrickville', 'fourth', 'business', 'closed', 'covid', 'safety', 'breaches'] ['close', 'wednesday'] ['inspectors', 'visited', 'three', 'times', 'observed', 'breaches', 'relating', 'their', 'covid', 'safety', 'overbooking', 'groups', 'gaming', 'machine', 'spacing', 'inadequate', 'check', 'processes', 'argeres', 'tuesday'] ['since', 'closure', 'order', 'issued', 'however', 'things', 'fined', 'longer', 'breaches', 'example', 'bookings', 'people', 'rather', 'while', 'guests', 'spaced', 'least', 'metres', 'apart', 'gaming', 'machines'] ['changed', 'having', 'robust', 'digital', 'check', 'processes', 'allow', 'effective', 'contact', 'tracing', 'event', 'positive', 'visited', 'venue', 'argeres'] ['service', 'covid', 'check', 'businesses', 'registered', 'covid', 'anyone', 'check', 'using', 'service', 'account'] ['inspectors', 'liquor', 'gaming', 'trading', 'safework', 'conducted', 'covid', 'safety', 'visits', 'issued', 'penalties', 'worth', 'temporarily', 'closed', 'businesses'] ['newspaper'] ['tests', 'underway', 'covid', 'treatment'] ['australian', 'associated', 'press'] ['published', 'august', 'updated', 'august'] ['australian', 'researchers', 'testing', 'treatment', 'coronavirus', 'provide', 'relief', 'those', 'infected', 'covid', 'limit', 'spread'] ['professor', 'david', 'morris', 'international', 'adapted', 'cancer', 'treatment', 'known', 'bromac', 'could', 'prevent', 'virus', 'moving', 'lungs', 'carriers', 'spreading', 'other', 'people'] ['trial', 'coronavirus', 'patients', 'being', 'established', 'melbourne', 'hospital', 'could', 'start', 'month'] ['bromac', 'under', 'development', 'years', 'cancer', 'treatment', 'includes', 'components', 'together', 'dissolve', 'spike', 'covid', 'rendering', 'unable', 'infect', 'other', 'cells'] ['agents', 'pineapple', 'enzyme', 'tested', 'after', 'observed', 'eating', 'pineapples', 'resistant', 'particular', 'gastro', 'conditions'] ['taken', 'development', 'decade', 'asked', 'whether', 'adapted', 'treating', 'people', 'infected', 'covid', 'professor', 'david', 'morris', 'statement', 'monday'] ['results', 'renders', 'covid', 'spike', 'ineffective', 'stopping', 'infecting', 'other', 'cells'] ['results', 'treatment', 'confine', 'covid', 'throat', 'prevent', 'infection', 'infected', 'patients', 'passing', 'virus'] ['hoped', 'infected', 'person', 'could', 'medication', 'first', 'signs', 'covid', 'virus', 'tracks'] ['could', 'provide', 'effective', 'saving', 'lives', 'protecting', 'vulnerable', 'workers', 'preventing', 'hospitalisation', 'getting', 'people', 'infected', 'covid', 'returning', 'their', 'everyday', 'lives'] ['researchers', 'stress', 'treatment', 'vaccine', 'covid', 'could', 'tandem', 'treatment', 'prevention', 'measure']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'trial': -0.46149416344279787,
'vaccin': -0.3811194487810655,
'stage': -0.3279128258377103,
'expect': -0.2328358984915442,
'dose': -0.19987854999141674}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph19.png')
# LSA Model
number_of_topics = 20 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_non_science =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_non_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled22.png')
cosine-similarity
trial vaccin stage expect dose
trial 1 0.234131 0.968477 0.899478 0.752671
vaccin 0.234131 1 0.250697 0.297949 0.630685
stage 0.968477 0.250697 1 0.951998 0.784867
expect 0.899478 0.297949 0.951998 1 0.79207
dose 0.752671 0.630685 0.784867 0.79207 1
document_list,titles,word_count=load_data("","covid-psychology.txt")
Number of words in text file : 86550 Total Number of Documents: 9007
clean_text=preprocess_data(document_list)
['international', 'sociology'] [] ['author'] ['article', 'reuse', 'guidelines'] ['sagepub', 'journals', 'permissions'] ['0268580920948807'] ['journals', 'sagepub'] ['psychology', 'politics'] ['covid', 'misinfodemics'] ['people'] ['believe', 'misinfodemics'] ['sonia', 'mukhtar'] ['university', 'management', 'technology', 'lahore', 'pakistan'] ['abstract'] ['misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse'] ['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate'] ['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects'] ['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier'] ['about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article'] ['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects'] ['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article'] ['shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product'] ['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive'] ['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations'] ['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications'] ['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health'] ['keywords'] ['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science'] ['mechanisms', 'misinfodemics'] ['modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding'] ['corresponding', 'author'] ['sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore'] ['punjab', '54770', 'pakistan'] ['email', 'sonia', 'mukhtar12', 'gmail'] ['948807iss0010', '0268580920948807international', 'sociologymukhtar'] ['research', 'article2020'] ['article'] ['international', 'sociology'] ['covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus'] ['covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern'] ['globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin'] ['unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during'] ['lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication'] ['become', 'major', 'source', 'interaction', 'holman'] ['covid', 'pandemic', 'outbreak', 'escalated', 'challenges'] ['healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable'] ['misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories'] ['claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science'] ['times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false'] ['context', 'manipulated', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease'] ['mukhtar', '2020a'] ['covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms'] ['become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated'] ['these', 'rumors', 'caused', 'several', 'mishaps', 'across', 'world', 'instance'] ['after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'overdosed'] ['cholorquine', 'after', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', 'there', 'failure'] ['distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmed'] ['science', 'conspiracy', 'theories', 'among', 'general', 'public', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'vaccine', 'covid'] ['create', 'social', 'stigma', 'resulting', 'xenophobia', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'adverse', 'health'] ['psychosocial', 'impacts', 'aguilera', '2020a', '2020b', 'these', 'considerations', 'become', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe'] ['popular', 'opinion', 'spend', 'social', 'media', 'people', 'trying'] ['sense', 'their', 'changed', 'lives', 'proclaimed', 'celebrities', 'identified', 'social'] ['media', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'their'] ['subjective', 'interpretations', 'events', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer'] ['scientific', 'studies', 'previous', 'disease', 'outbreaks', 'demonstrated', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', 'kalichman', 'individuals', 'endorse'] ['misinformation', 'about', 'disease', 'likely', 'follow', 'public', 'health', 'instructions', 'during'] ['ebola', 'outbreak', 'respondents', 'credence', 'conspiracy', 'theories'] ['alleged', 'would', 'likely', 'support', 'during', 'disease', 'outbreak'] ['national', 'cancer', 'institute', 'study', 'montanaro', 'conducted'] ['respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'information'] ['about', 'covid', 'current', 'administration', 'mainstream', 'media', 'outlets'] ['times'] ['mukhtar'] ['psychology', 'misinfodemics'] ['adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actual'] ['perceived', 'threat', 'mertens', 'however', 'excessive'] ['detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'anxiety'] ['phobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia'] ['insufficient', 'cause', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation'] ['policies', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonal'] ['safety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocial'] ['issues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact'] ['efforts', 'misinfodemics', 'process', 'increased', 'because', 'adverse'] ['effects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainable'] ['preventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health'] ['resilience', 'socioeconomic', 'conditions', 'address', 'stigma', 'prejudice', 'discrimination'] ['inequalities', 'mukhtar', '2020b', 'among', 'various', 'factors', 'psychological', 'vulnerability', 'mukhtar', '2020c', 'mukhtar', 'mahmood', 'propensity', 'follow', 'popular'] ['opinion', 'excessive', 'social', 'media', 'presence', 'predisposition', 'anxiety'] ['uncertainty', 'intolerance', 'unpredictability', 'coronavirus', 'brings', 'levels', 'uncertainty', 'inability', 'uncertainty', 'exponentially', 'higher', 'causing', 'anxiety'] ['health', 'related', 'worry', 'situation', 'exacerbated', 'exposure', 'plethora'] ['information', 'including', 'misinformation', 'disinformation', 'about', 'impending'] ['threat', 'mainstream', 'media', 'social', 'media', 'alike', 'rosser', 'perceived', 'threat'] ['information', 'elevates', 'repeated', 'engagement', 'trauma', 'related', 'media', 'content'] ['several', 'hours', 'daily', 'culminates', 'acute', 'stress', 'emotional', 'distress', 'consequently'] ['either', 'increase', 'virus', 'cause', 'insensitivity', 'towards', 'course'] ['identification', 'monitoring', 'internet', 'centrifugal', 'clarification', 'filter'] ['accuracy', 'content', 'become', 'challenging', 'increasing', 'number', 'people'] ['social', 'media', 'platforms', 'challenging', 'problems', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'social', 'media', 'emulsify', 'false'] ['misleading', 'click', 'content', 'world', 'health', 'organization', 'launched'] ['mythbuster', 'feature', 'website', 'countermeasure', 'spread', 'unauthentic'] ['social', 'media', 'people', 'reported', 'highest', 'media', 'exposure', 'reported', 'higher'] ['acute', 'stress', 'holman', 'media', 'exposure', 'accumulates', 'emerging', 'threats'] ['repeated', 'exposure', 'these', 'events', 'increase', 'symptoms', 'distress', 'worry', 'fixation'] ['mentality', 'misplaced', 'conviction', 'people', 'vulnerable', 'media', 'exposure', 'distress'] ['alongside', 'amount', 'media', 'exposure', 'content', 'exposure', 'matters'] ['exposure', 'tragic', 'events', 'graphic', 'images', 'conspiracy', 'theories', 'violence', 'could'] ['instigate', 'posttraumatic', 'stress', 'future', 'which', 'personal'] ['functioning', 'recent', 'years', 'mainstream', 'media', 'electronic', 'print', 'social', 'media'] ['established', 'themselves', 'beyond', 'government', 'channel', 'global'] ['international', 'sociology'] ['population', 'developed', 'affinity', 'channels', 'particular', 'while', 'older'] ['population', 'mainly', 'relies', 'traditional', 'channels', 'youth', 'social', 'media'] ['tiktok', 'instagram', 'youtube', 'twitter', 'facebook', 'whatsapp', 'social', 'media'] ['become', 'conduit', 'spreading', 'rumors', 'deliberate', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'personally', 'motivated', 'anecdotes', 'appeal', 'followers'] ['attract', 'attention', 'create', 'panic', 'liang', 'proclaimed', 'celebrities', 'media', 'stars', 'become', 'influential', 'large', 'number', 'people', 'solely'] ['their', 'content', 'merchant', 'social', 'media', 'posts', 'circulating'] ['platforms', 'endorsed', 'multiple', 'social', 'media', 'account', 'users', 'encourage', 'optimal'] ['personal', 'functioning', 'maximum', 'productivity', 'business', 'oriented', 'activities', 'downplaying', 'intensity', 'traumatic', 'event', 'certain', 'posts'] ['quarantine', 'skill', 'hustle', 'started', 'knowledge'] ['never', 'lacked', 'lacked', 'discipline', 'circulating', 'various', 'social', 'media', 'accounts'] ['prompting', 'people', 'utilize', 'their', 'learning', 'things', 'skills', 'exacerbate'] ['worry', 'already', 'anxiety', 'prone', 'individuals', 'place', 'psychological', 'pressure', 'productive', 'rather', 'messages', 'quarantine', 'skill'] ['hustle', 'started', 'knowledge', 'doing', 'should', 'disseminated', 'social', 'media', 'individuals', 'realize', 'during', 'intense', 'traumatic', 'event', 'everyone', 'equally', 'endowed', 'strength', 'coping', 'strategies'] ['problem', 'solving', 'skills', 'transform', 'trauma', 'positive', 'emotion', 'which'] ['source', 'shame', 'guilt'] ['there', 'coercive', 'impression', 'percolating', 'among', 'individuals', 'regarding', 'lockdown'] ['people', 'should', 'assume', 'holiday', 'utilize', 'their', 'productively', 'engage', 'occupational', 'academic', 'activities', 'psychological', 'pressure'] ['further', 'aggravated', 'feelings', 'guilt', 'shame', 'regret', 'sadness', 'anger', 'internalized'] ['emotions', 'being', 'overwhelmed', 'negative', 'unrealistic', 'expectations', 'perceived', 'sense', 'failure', 'mukhtar', '2020a', '2020b', 'psychological', 'pressure', 'compete', 'peers', 'achieving', 'maximum', 'tasks', 'producing', 'occupational', 'academic'] ['outcomes', 'harnessing', 'followers', 'subscribers', 'media', 'accounts'] ['downplaying', 'trauma', 'others', 'devastating', 'effects', 'mental'] ['wellbeing'] ['lockdown', 'resulting', 'isolation', 'quarantine', 'social', 'distancing', 'removed'] ['normal', 'leisure', 'might', 'utilized', 'improved', 'personal', 'functioning'] ['collective', 'traumatic', 'event', 'which', 'poses', 'serious', 'threat', 'people', 'resulted'] ['lives', 'displacement', 'individuals', 'mukhtar', 'mukhtar', 'mukhtar'] ['covid', 'individual', 'collective', 'traumatic', 'event', 'directly'] ['indirectly', 'affected', 'every', 'individual', 'world', 'efforts', 'should', 'directed', 'towards'] ['minimizing', 'negative', 'effects', 'traumatic', 'covid', 'pandemic', 'survivors'] ['people', 'going', 'through', 'interpersonal', 'traumatic', 'events', 'addition', 'collective'] ['trauma', 'covid', 'domestic', 'violence', 'gender', 'based', 'violence', 'abuse', 'mukhtar', '2020d'] ['financial', 'burden', 'loneliness', 'emotional', 'behavioral', 'problems', 'grief', 'bereavement'] ['losing', 'family', 'mental', 'health', 'issues', 'physical', 'injuries', 'fatalities'] ['isolated', 'people', 'facing', 'psychological', 'issues', 'require', 'trauma', 'focused', 'psychological'] ['support', 'mental', 'health', 'psychological', 'support', 'guidance', 'treatment', 'intervention'] ['information', 'psychological', 'cyber', 'counseling', 'smartphone'] ['mukhtar'] ['instance', 'mental', 'health', 'hotlines', 'professional', 'seeking', 'behaviors', 'should'] ['encouraged', 'endorsed', 'related', 'barriers', 'stigma', 'marginalization', 'discrimination', 'shaming', 'phobias', 'should', 'discouraged', 'through', 'government', 'driven', 'programs'] ['evidence', 'based', 'treatment', 'models'] ['emerging', 'pandemics', 'command', 'intellectual', 'incapacity', 'decision', 'making'] ['degree', 'irrationality', 'bombarded', 'conflicting', 'opinions', 'conspiracy', 'theories', 'empirical', 'interpretation', 'origins', 'impacts', 'result', 'moral', 'judgments'] ['based', 'religious', 'cultural', 'beliefs', 'emergence', 'garnered', 'plethora'] ['moral', 'judgments', 'orbiting', 'around', 'sexual', 'morality', 'substance', 'abuse', 'arrival'] ['blame', 'directed', 'towards', 'primitive', 'farming', 'practices', 'guangzhou'] ['unprecedented', 'covid', 'pandemic', 'welcomed', 'magnitude', 'misinfodemics'] ['multitude', 'fronts', 'ophir', 'richtel', 'psychological', 'predisposition', 'reject'] ['authorized', 'information', 'through', 'denial', 'byproduct', 'founded', 'mistrust'] ['tendency', 'major', 'social', 'political', 'events', 'through', 'conspiracies', 'partisan', 'motivations', 'byproduct', 'conspiracy', 'thinking', 'likely', 'explanatory', 'factors', 'understanding', 'believes', 'covid', 'misinfodemics', 'association', 'between'] ['conspiracy', 'thinking', 'defense', 'mechanism', 'denial', 'could', 'potential', 'reason'] ['refusal', 'corrective', 'actions', 'several', 'health', 'related', 'cases', 'carey'] ['misinfodemics', 'could', 'motivated', 'various', 'factors', 'epistemic', 'desire', 'causal'] ['explanation', 'subjective', 'certainty', 'existential', 'desire', 'control', 'security'] ['social', 'desire', 'maintain', 'positive', 'image', 'group', 'douglas'] ['stigmatization', 'labeling', 'scapegoating', 'swiftly', 'follow', 'narrative', 'which'] ['invokes', 'vernacular', 'human', 'conflict', 'waging', 'against', 'killer', 'virus', 'indomitable', 'invisible', 'threat', 'enemy', 'armed', 'vaccine', 'flinging', 'victims', 'quarantine'] ['camps', 'losing', 'sense', 'proportion', 'ability', 'mitigate', 'reasonable'] ['knowledge', 'based', 'measures', 'challenge', 'normalize', 'perceived', 'threat', 'infection'] ['mechanism', 'misinfodemics', 'engrossed', 'silos', 'public', 'information', 'which'] ['inept', 'limiting', 'risks', 'instance', 'social', 'theme', 'focusing', 'socioeconomic', 'disruption', 'scientific', 'theme', 'focusing', 'medical', 'health', 'communication', 'pandemic', 'theme', 'focusing', 'state', 'global', 'response', 'populations'] ['likely', 'affected', 'emerging', 'disease', 'pandemic', 'simultaneous', 'misinfodemics'] ['include', 'those', 'where', 'there', 'disproportionally', 'inadequate', 'health', 'literacy', 'disadvantaged', 'socioeconomic', 'groups', 'migrants', 'ethnic', 'minorities', 'vulnerable', 'groups'] ['including', 'older', 'people', 'people', 'chronic', 'health', 'conditions', 'people', 'disability'] ['mukhtar', '2020e', 'rowlands'] ['politics', 'coronavirus'] ['conspiracy', 'theories', 'heavily', 'influenced', 'geopolitics', 'spread', 'regarding', 'origin'] ['scale', 'prevention', 'treatment', 'diagnosis', 'disease', 'covid', 'being', 'viral'] ['bioweapon', 'genetically', 'engineered', 'rogue', 'government', 'racist', 'genocidal'] ['agenda', 'economic', 'psychological', 'chinese', 'biological', 'weapon'] ['conspiracy', 'theory', 'which', 'spread', 'throughout', 'united', 'states', 'united', 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'linear', 'version', 'autoregressive'] ['distributed', 'model', 'applied'] ['methodology'] [] ['study', 'utilized', 'daily', 'number', 'confirmed'] ['patients', 'deaths', 'covid', 'stock', 'markets'] ['spain', 'italy', 'germany', 'period'] ['february', 'april', 'study', 'taken'] ['database', 'yahoo', 'finance', 'european', 'center'] ['disease', 'control', 'prevention', 'further', 'investigation'] ['analyzed', 'daily', 'returns'] ['london', 'stock', 'exchange', 'nomination', 'spain', 'italy'] ['germany', 'however', 'nasdaq', 'composite'] ['index', 'represents'] ['methodology'] ['investigation', 'utilized', 'stock', 'market', 'daily', 'returns'] ['dependent', 'variable', 'confirmed', 'cases', 'deaths'] ['independent', 'variable', 'daily', 'returns', 'study'] ['computed', 'follows', 'syllignakis', 'kouretas', 'shehzad'] ['sohail'] [] [] [] ['denote', 'return', 'value', 'closing'] ['price', 'previous', 'closing', 'price'] ['stock', 'market', 'respectively', 'linear', 'association', 'between', 'these'] ['variables', 'defined'] ['1sprt', 'β1usct', 'β2usdt'] ['1nsrt', 'β1usct', 'β2usdt'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['figure', 'deterioration', 'stock', 'markets', 'owing', 'novel', 'coronavirus'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['figure', 'variations', 'global', 'financial', 'crisis'] ['covid', 'crisis'] ['1ibert', 'β1spct', 'β2spdt'] ['1ftmrt', 'β1itlct', 'β2itldt'] ['1daxrt', 'β1gerct', 'β2gerdt'] ['1lsert', 'β1ukct', 'β2ukdt'] ['where', 'signifies', 'first', 'difference', 'β0β1β2'] ['independent', 'parameters', 'however'] ['defines', 'return', 'values', 'nasdaq'] ['composite', 'index'] ['london', 'stock', 'exchange', 'respectively', 'moreover'] ['indicate', 'confirmed', 'cases', 'covid', 'spain', 'italy', 'germany', 'respectively'] ['further', 'refer', 'confirmed'] ['deaths', 'covid', 'spain', 'italy', 'germany'] ['respectively'] ['ascertain', 'short', 'asymmetries'] ['study', 'employed', 'linear', 'autoregressive', 'distributed'] ['nardl', 'model', 'introduced', 'model'] ['performs', 'small', 'number', 'observations'] ['applied', 'mixed', 'level', 'stationary'] ['evaluates', 'linearity', 'cointegration', 'between', 'variables'] ['equation', 'shared', 'short', 'variations'] ['asymmetries', 'after', 'taking', 'parameters', 'account'] ['nardl', 'model', 'extended'] ['pesaran', 'model', 'hence'] ['nardl', 'model', 'stock', 'market', 'specified'] ['1sprt'] [] [] [] ['1sprt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ2ausc'] ['γ2busc'] ['γ3ausd'] [] [] [] ['1nsrt'] [] [] [] ['1nsrt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ1nsrt', 'γ2ausc'] ['γ2busc'] [] [] ['γ3busd'] [] ['1ibert'] [] [] [] ['1ibert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ1ibert', 'γ2aspc'] ['γ2bspc'] [] [] ['γ3bspd'] [] ['1ftmrt'] [] [] [] ['1ftmrt'] [] [] [] ['1itlc'] [] [] [] [] [] ['1itlc'] [] [] [] [] [] ['1itld'] [] [] [] [] [] [] ['γ1ftmrt', 'γ2aitlc'] [] [] [] [] [] [] [] ['1daxrt'] [] [] [] ['1daxrt'] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerc'] [] [] [] [] ['1gerd'] [] [] [] [] [] [] ['γ1daxrt', 'γ2agerc'] ['γ2bgerc'] [] [] ['γ3bgerd'] [] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['1lsert'] [] [] [] ['1lsert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ1lsert', 'γ2aukc'] ['γ2bukc'] [] [] ['γ3bukd'] [] ['ϑ2bϑ3aand', 'short', 'factors', 'while'] [] [] ['indicates', 'parameters'] ['whereas', 'symbolizes', 'number', 'model'] ['based', 'akaik', 'information', 'criterion', 'schwarz', 'information'] ['criterion', 'above', 'stated', 'equations', 'assumed'] ['confirmed', 'number', 'cases', 'deaths', 'covid'] ['asymmetrical', 'impact', 'stock', 'returns'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['designates', 'positive', 'shock', 'though'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['implies', 'negative', 'shock', 'variable'] ['computed', 'follows'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['delineates', 'positive', 'shock', 'variable', 'denotes'] ['asymmetric', 'distributive', 'error', 'correction'] ['these', 'factors', 'written', 'follows'] ['1sprt'] [] [] [] ['1sprt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['1nsrt'] [] [] [] ['1nsrt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['1ibert'] [] [] [] ['1ibert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['1ftmrt'] [] [] [] ['1ftmrt'] [] [] [] ['1itlc'] [] [] [] [] [] ['1itlc'] [] [] [] [] [] ['1itld'] [] [] [] [] [] [] ['φ1ectt'] ['1daxrt'] [] [] [] ['1daxrt'] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerd'] [] [] [] [] [] [] ['φ1ectt'] ['1lsert'] [] [] [] ['1lsert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['directs', 'error', 'correction'] ['cointegration', 'among', 'variables', 'examined', 'through', 'bound'] ['approach', 'pesaran', 'method', 'relies', 'evaluate', 'hypothesis'] ['purpose', 'pesaran', 'defined', 'bounds'] ['upper', 'lower', 'bound', 'estimated', 'statistics'] ['higher', 'upper', 'bound', 'limit', 'hypothesis'] ['cointegration', 'rejected', 'however', 'projected', 'value', 'lower', 'lower', 'bound', 'limit', 'hypothesis', 'cannot'] ['rejected', 'besides', 'value', 'remains', 'between'] ['limits', 'results', 'conclusive', 'project'] ['employed', 'verify', 'asymmetric'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['andshort'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['asymmetric'] ['relationship', 'between', 'study', 'variables'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['study', 'findings'] ['descriptive', 'summary', 'showed', 'table', 'during', 'covid', 'stock', 'returns', 'negative', 'further', 'skewness'] ['values', 'these', 'markets', 'negative', 'except'] ['kurtosis', 'which', 'predicts', 'chances', 'these'] ['table', 'presents', 'results', 'augmented', 'dickey', 'fuller'] ['dickey', 'fuller', 'phillip', 'perron'] ['phillips', 'perron', 'showed', 'study', 'variables'] ['diverse', 'stationary', 'levels', 'variable'] ['cointegrated'] ['bound', 'cointegration', 'fallouts'] ['outcomes', 'bound', 'displayed', 'table', 'disclosed'] ['significant', 'statistical', 'evidence', 'association', 'between'] ['study', 'variables', 'equation', 'statistics', 'joint'] ['significance', 'lagged', 'level', 'parameters', 'stated'] ['respectively', 'these', 'values'] ['surpass', 'upper', 'bound', 'limits', 'implying'] ['cointegration', 'exists', 'among', 'economic', 'crisis', 'health'] ['crisis', 'financial', 'markets', 'equation'] ['short', 'asymmetric'] ['cointegration', 'reckoning'] ['table', 'exhibited', 'upshots', 'statistics'] ['particularized', 'linear', 'association'] ['however', 'revealed', 'short', 'asymmetric'] ['affiliation', 'besides', 'caused', 'asymmetric'] ['influence', 'short', 'periods'] ['while', 'possessed', 'short', 'asymmetry', 'additionally'] ['table', 'descriptive', 'statistics'] [] ['41737', '08997', '70833', '28187'] ['skewness', '68003', '687765', '641313', '027343', '392073', '081854', '35422', '186996'] ['kurtosis', '36907', '166164', '206693', '903565', '778953', '75376', '380136', '389965'] ['jarque', '26244', '69524', '87966', '47298', '76708'] ['probability', '00434', '000161'] [] ['18523', '54717', '42086', '40245'] ['skewness', '927378', '53985', '120857', '230936', '65869', '232889', '058817', '31595'] ['kurtosis', '43888', '59924', '573906', '120135', '135106', '868864', '472667', '04434'] ['jarque', '44518', '49843', '45175', '59309', '03773', '08892'] ['probability', '000013', '003185', '000121', '000872'] ['source', 'author', 'calculation'] ['table'] ['statistics', 'statistics'] ['variable', 'level', 'level'] ['893713', '54426', '057616'] ['293963', '969976', '971366', '51473'] ['935247', '359161', '147762', '393867'] ['631659', '55646', '451458'] ['127702', '174362', '400067', '35456'] ['300231', '849862', '488626', '372171'] ['27056', '985616', '446574', '65801'] ['506727', '32241', '159422', '09496'] ['552953', '028486', '374729', '97616'] ['775255', '591517', '768878', '87539'] ['816325', '578091', '746256', '61778'] ['429147', '36282', '571441', '31254'] ['89506', '022076', '13962'] ['847753', '567572', '310638', '46537'] ['138569', '097646', '384682', '006879'] ['66594', '40744', '64235', '28961'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'results', 'bound'] ['statistic', 'value', 'signif', 'statistics', 'value', 'signif'] [] ['statistic', '02410', 'statistic', '165881'] [] [] [] ['statistic', '11238', 'statistic', '23325'] [] [] [] ['statistic', '490181', 'statistic', '03259'] [] [] ['source', 'author', 'calculation'] ['table'] ['asymmetry', 'short', 'asymmetry', 'asymmetry', 'short', 'asymmetry'] [] ['242605'] [] [] ['574046'] ['589403', '609162', '338933'] [] ['569179', '725822', '640751'] ['61007', '786146', '718125'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['results', 'designated', 'asymmetric'] ['relationship', 'statistics', 'enlightened'] ['short', 'linear', 'impacts'] ['short', 'linear', 'affiliation'] ['moreover', 'denotes', 'asymmetric', 'liaison'] ['hence', 'existence', 'linearity', 'study', 'variables'] ['choose', 'nardl', 'model', 'computing'] ['short', 'factors'] ['modeling', 'nardl', 'parameters'] ['linear', 'fallouts', 'presented', 'table', 'illustrated'] ['expansion', 'contraction', 'brings', 'reduction'] ['increase', 'respectively', 'indicates', 'strict', 'lockdown'] ['substantially', 'decreases', 'market', 'returns'] ['versa', 'further', 'positive', 'shock', 'caused', 'significant'] ['short', 'lagged', 'values', 'specified'] ['negative', 'shock', 'amplified', 'besides', 'lagged'] ['terms', 'described', 'uptick', 'effect', 'improved'] ['negative', 'shock', 'second', 'augmented'] ['first', 'third', 'represents', 'reverse', 'impact'] ['furthermore', 'negative', 'significant', 'value', 'stated'] ['disequilibrium', 'occurred', 'today', 'covid'] ['adjust', 'speed', 'units', 'subsequent'] ['squared', 'value', 'indicated', 'volatility'] ['owing', 'covid'] ['model', 'results', 'exhibited', 'table', 'particularized'] ['positive', 'negative', 'shocks', 'escalate'] ['while', 'enlargement', 'reduction', 'instigate', 'decrease'] ['increase', 'specifies', 'upsurge', 'health', 'crisis'] ['expressively', 'distressed', 'nasdaq', 'composite', 'index'] ['versa', 'nonetheless', 'short', 'negative', 'shock'] ['demonstrated', 'imperative', 'decrease'] ['whereas', 'expansion', 'lessens', 'negative'] ['significant', 'parameter', 'stated', 'financial', 'instability'] ['generated', 'covid', 'would', 'settled', 'speed'] ['units', 'square', 'value', 'signified'] ['instability', 'occurring', 'result', 'covid'] ['finding', 'parameters', 'model', 'table', 'identified'] ['assertive', 'shock', 'encouraging', 'effect'] ['besides', 'adverse', 'shocks', 'remain', 'insignificant', 'other'] ['escalation', 'decline', 'negative', 'positive', 'influence'] ['implying', 'strict', 'lockdown', 'considerably', 'caused'] ['financial', 'crisis', 'spain', 'period', 'short'] ['period', 'intensification', 'decline'] ['increased', 'negative', 'significant', 'value'] ['elucidated', 'today', 'disequilibrium', 'adjust'] ['speed', 'units', 'coefficient', 'square'] ['stated', 'variations', 'owing', 'covid'] ['table', 'displayed', 'fallouts', 'stock', 'market'] ['condition', 'covid', 'italy', 'stock', 'markets', 'exposed'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['usc_neg', '047867', '003539', '52394'] ['usc_neg', '061323', '003402', '02596'] ['usc_neg', '108895', '005569', '55339'] ['usc_neg', '072915', '003619', '14618'] ['usd_pos', '713848', '036514', '55011'] ['usd_pos', '23431', '014268', '42276'] ['usd_pos', '194744', '01369', '22562'] ['usd_pos', '334891', '029691', '27907'] ['usd_neg', '377433', '055371', '816426'] ['usd_neg', '65514', '097958', '89641'] ['usd_neg', '074692', '02737', '728986'] ['usd_neg', '650475', '235321', '51275'] ['369525', '066813', '49782'] ['squared', '912909'] ['adjusted', 'squared', '892007'] ['durbin', 'watson', '006439'] ['variables', 'coefficients', 'error'] ['usc_pos', '004491', '001667', '694157'] ['usc_neg', '054381', '014406', '774912'] ['usd_pos', '064436', '029631', '174626'] ['usd_neg', '290139', '338048', '858278'] ['181416', '264851', '684971'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['usd_neg', '039324', '019503', '016267'] ['usc_pos', '002147', '000382', '622014'] ['usc_neg', '002319', '00069', '360414'] ['usc_neg', '008198', '001064', '703282'] ['501702', '093855', '00026'] ['squared', '812596'] ['adjusted', 'squared', '800102'] ['durbin', 'watson', '130245'] ['variables', 'coefficients', 'error'] ['usd_pos', '013042', '004465', '920922'] ['usd_neg', '026413', '030567', '864107'] ['usc_pos', '001013', '00034', '977381'] ['usc_neg', '004258', '001923', '214416'] ['525513', '274541', '914153'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['reduction', 'diminished', 'amplified'] ['inferring', 'heath', 'crisis', 'imperatively', 'contributes', 'italy'] ['financial', 'instability', 'period', 'addition', 'growth'] ['enlarged', 'imperatively', 'condition'] ['italy', 'denoted', 'first', 'second', 'third', 'values'] ['harmed', 'returns', 'growth', 'negative'] ['impression', 'nonetheless', 'negative', 'shock'] ['second', 'value', 'indicated', 'encouraging', 'effect'] ['consequences', 'symbolized', 'proliferation'] ['possess', 'negative', 'ftmer', 'first'] ['second', 'value', 'definite', 'furthermore', 'decline'] ['direct', 'influence', 'negative'] ['significant', 'figure', 'itemized', 'readjust'] ['equilibrium', 'speed', 'units', 'squared', 'parameter', 'denoted', 'uncertainty', 'generated'] ['caused', 'covid'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['368944', '09522'] ['spd_pos', '018471', '009268'] ['spd_neg', '026216', '010675'] ['spc_pos', '001511', '000911'] ['spc_neg', '001832', '000966'] ['spc_neg', '00301', '000996'] ['spc_neg', '006948', '00129'] ['824331', '137511'] ['squared', '726141'] ['adjusted', 'squared', '694191'] ['durbin', 'watson', '154368'] ['variables', 'coefficients', 'error'] ['spd_pos', '053991', '01765'] ['spd_neg', '023488', '018017'] ['spc_pos', '006341', '001968'] ['spc_neg', '005173', '001945'] ['636028', '428132'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['27947', '138377', '019624'] ['154875', '136743', '132605'] ['295127', '113398', '602569'] ['itlc_pos', '008711', '001397', '235569'] ['itlc_pos', '015154', '003264', '642728'] ['itlc_pos', '002589', '573306'] ['itlc_neg', '001115', '001937', '575378'] ['itlc_neg', '006885', '00198', '477965'] ['itlc_neg', '003758', '001782', '109409'] ['itld_pos', '06983', '014714', '745672'] ['itld_pos', '023801', '011962', '989722'] ['itld_pos', '029575', '010511', '813763'] ['itld_neg', '002565', '012862', '199404'] ['itld_neg', '038093', '015194', '507186'] ['itld_neg', '04502', '013664', '294772'] ['959373', '163657', '862111'] ['squared', '851016'] ['adjusted', 'squared', '807197'] ['durbin', 'watson', '763297'] ['variables', 'coefficients', 'error'] ['itlc_pos', '013317', '005649', '357295'] ['itlc_neg', '001321', '002682', '492464'] ['itld_pos', '109433', '053273', '054209'] ['itld_neg', '048345', '020559', '351573'] ['500777', '455916', '098396'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['parameters', 'table', 'directed'] ['upsurge', 'diminution', 'compacted'] ['enlarge', 'moreover', 'decline'] ['improves', 'these', 'findings', 'revealed', 'lockdown'] ['health', 'crises', 'harmful', 'stock', 'markets'] ['germany', 'short', 'findings', 'described', 'second'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['gerc_pos', '015476', '001209', '80122'] ['gerc_pos', '012439', '001923', '467043'] ['gerc_pos', '088025', '005402', '29596'] ['gerc_pos', '057992', '003854', '04828'] ['gerc_pos', '00647', '99249'] ['gerc_neg', '05202', '004721', '11903'] ['gerc_neg', '067796', '004325', '67443'] ['gerc_neg', '044564', '003649', '21376'] ['gerc_neg', '01327', '004158', '191441'] ['gerc_neg', '183056', '01145', '98705'] ['gerd_pos', '101477', '313432', '27619'] ['gerd_pos', '265597', '35462', '84858'] ['gerd_pos', '054459', '100825', '540134'] ['gerd_pos', '261756', '386781', '18939'] ['gerd_neg', '913296', '288936', '54383'] ['gerd_neg', '64757', '850055', '56576'] ['gerd_neg', '76795', '466544', '20677'] ['gerd_neg', '21192', '08761'] ['497359', '029762', '71134'] ['squared', '945788'] ['adjusted', 'squared', '910937'] ['durbin', 'watson', '667758'] ['variables', 'coefficients', 'error'] ['gerc_pos', '016748', '035958', '465776'] ['gerc_neg', '255912', '083972'] ['gerd_pos', '28734', '569148', '548151'] ['gerd_neg', '49979', '581496'] ['440744', '93232', '617925'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['ukc_pos', '00127', '001661', '764723'] ['ukc_pos', '022345', '003248', '878603'] ['ukc_neg', '009267', '002137', '337228'] ['ukd_pos', '03503', '00746', '695883'] ['ukd_pos', '090212', '014812', '090388'] ['100754', '115104', '563147'] ['squared', '651816'] ['adjusted', 'squared', '624182'] ['durbin', 'watson', '867108'] ['variables', 'coefficients', 'error'] ['ukc_pos', '003609', '00191', '889866'] ['ukc_neg', '003213', '002112', '521044'] ['ukd_pos', '075038', '02864', '620078'] ['ukd_neg', '086022', '035056', '453823'] ['319529', '353596', '903654'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['third', 'fourth', 'positive', 'shocks'] ['lessening', 'impact', 'however', 'negative', 'shock'] ['other', 'values', 'except', 'first', 'indicated'] ['indirect', 'association', 'additionally', 'positive'] ['shocks', 'quantified', 'mixed', 'impact'] ['while', 'short', 'decline', 'negative', 'first'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['second', 'third', 'values', 'point', 'positive'] ['linkage', 'these', 'effects', 'because', 'instability'] ['generated', 'covid', 'squared', 'coefficient'] ['diagnosed', 'instability', 'because'] ['covid'] ['table', 'represents', 'results', 'london', 'stock'] ['exchange', 'concluding', 'coefficients', 'explored'] ['augmentation', 'improved', 'while', 'diminution'] ['reduces', 'period', 'although'] ['growth', 'brought', 'significant', 'decrease'] ['short', 'period', 'outlined', 'positive', 'shocks'] ['reduced', 'first', 'value', 'positive', 'shock'] ['showed', 'alternate', 'impression', 'additionally'] ['upturn', 'possesses', 'diminishing', 'impact'] ['these', 'outcomes', 'designated', 'lockdown', 'health', 'crises'] ['generated', 'covid', 'negatively', 'impact', 'financial'] ['markets', 'short', 'period', 'value'] ['showed', 'fluxes', 'occurred', 'today'] ['equilibrium', 'speed', 'units'] ['square', 'value', 'reported', 'variation'] ['covid', 'figure', 'depicts', 'adjustment', 'asymmetric'] ['effect', 'existing', 'equilibrium', 'moved'] ['equilibrium', 'result', 'positive', 'adverse'] ['shocks', 'asymmetric', 'plots', 'denote', 'alliance', 'dynamic'] ['multipliers', 'owing', 'positive', 'adverse', 'shocks'] ['nasdaq', 'composite', 'index'] ['markets', 'outcomes', 'revealed', 'these', 'markets', 'tremendously'] ['respond', 'positive', 'negative', 'shocks', 'befallen'] ['covid'] ['diagnostic', 'parameters', 'evaluation'] ['examination', 'employed', 'breusch', 'pagan'] ['diagnose', 'serial', 'correlation', 'heteroskedasticity'] ['model', 'residuals', 'ramsey', 'reset', 'technique', 'ascertain'] ['functional', 'misspecification', 'cusum', 'cusumsq', 'ploberger'] ['kramer', 'define', 'reliability', 'parameters'] ['conclusions', 'these', 'tests', 'given', 'table', 'nominated'] ['there', 'serial', 'correlation', 'heteroskedasticity'] ['residuals', 'model', 'besides', 'plots', 'cusum'] ['cusumsq', 'presented', 'figure', 'remain', 'within', 'critical'] ['boundaries', 'inferring', 'coefficients', 'nardl', 'model'] ['stable'] ['conclusion'] ['covid', 'imperatively', 'shaken', 'economic', 'indicators'] ['primarily', 'financial', 'markets', 'globe', 'number'] ['patients', 'deaths', 'result', 'covid', 'increasing'] ['economic', 'conditions', 'become', 'entirely'] ['uncertain', 'moreover', 'financial', 'markets', 'world', 'fronted'] ['sudden', 'crash', 'their', 'market', 'values', 'investigation'] ['utilized', 'linear', 'autoregressive', 'distributed', 'nardl'] ['approach', 'estimate', 'linear', 'impact', 'economic', 'crisis'] ['health', 'crisis', 'generated', 'result', 'covid'] ['pandemic', 'behavior', 'financial', 'markets'] ['infected', 'territories', 'europe', 'analysis', 'stated'] ['figure', 'dynamic', 'multiplier', 'plots'] ['returns', 'index', 'greatly', 'affected'] ['economic', 'crisis', 'generated', 'because', 'covid'] ['however', 'accession', 'health', 'crisis', 'because'] ['covid', 'significantly', 'decreased', 'nasdaq', 'composite', 'index'] ['returns', 'these', 'results', 'confirmed', 'economic', 'instability'] ['health', 'management', 'system', 'imperative', 'control'] ['financial', 'markets', 'behavior', 'verdicts', 'elaborated'] ['economic', 'crisis', 'produced', 'result', 'lockdown', 'circumstances'] ['spain', 'impact', 'compared'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'diagnostic', 'results'] ['heteroskedasticity', 'value', 'serial', 'correlation', 'value', 'heteroskedasticity', 'value', 'serial', 'correlation', 'value'] [] ['statistic', 'statistic', 'statistic', 'statistic'] ['ramsey', 'reset', 'statistic', 'ramsey', 'reset', 'statistic'] [] ['heteroskedasticity', 'serial', 'correlation', 'heteroskedasticity', 'serial', 'correlation'] ['statistic', 'statistic', 'statistic', 'statistic'] ['ramsey', 'reset', 'statistic', 'ramsey', 'reset', 'statistic'] [] ['heteroskedasticity', 'serial', 'correlation', 'heteroskedasticity', 'serial', 'correlation'] ['statistic', 'statistic', 'statistic', 'statistic'] ['ramsey', 'reset', 'statistic', 'ramsey', 'reset', 'statistic'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['figure', 'cusum', 'cusumsq', 'graph'] ['health', 'crisis', 'resulted', 'spain', 'italy'] ['stock', 'markets', 'affected', 'health', 'crisis'] ['economic', 'crisis', 'while', 'short', 'economic', 'uncertainty'] ['lowered', 'stock', 'returns', 'nevertheless'] ['health', 'crisis', 'germany', 'pointing', 'significant', 'upsurge'] ['economic', 'crisis', 'intimated', 'unimportant', 'impression'] ['index', 'stock', 'markets', 'recorded'] ['short', 'upsurge', 'health', 'crisis', 'imperatively'] ['damages', 'stock', 'returns', 'index'] ['influence', 'reverse', 'therefore'] ['research', 'authenticated', 'health', 'crisis', 'begun', 'covid', 'devised', 'another', 'global', 'financial', 'crisis', 'almost'] ['every', 'nation', 'fighting', 'these', 'crises', 'today', 'moreover'] ['study', 'disclosed', 'economic', 'uncertainty', 'generated'] ['covid', 'highest', 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'standardized', 'fractions', 'category', 'calculate', 'gradient'] ['python', 'function', 'numpy', 'gradient', 'gradient', 'provides', 'measure', 'increase', 'decrease'] ['signal', 'consider', 'absolute', 'value', 'gradient', 'account', 'magnitude', 'change', 'rather', 'direc', 'change', 'identify', 'periods', 'consistent', 'change', 'opposed', 'quick', 'instantaneous', 'shifts', 'apply', 'temporal'] ['smoothing', 'equation', 'series', 'gradients', 'denote', 'smoothed', 'squared', 'gradients'] [] ['average', 'gradients', 'language', 'categories', 'obtain', 'overall', 'gradient'] [] [] [] [] [] [] [] [] [] ['peaks', 'series', 'represent', 'highest', 'variation', 'marked', 'change', 'points', 'using'] ['python', 'function', 'scipy', 'signal', 'peaks', 'identified', 'peaks', 'local', 'maxima', 'whose', 'values', 'higher'] ['average', 'standard', 'deviations', 'common', 'practice', 'palshikar'] ['results'] ['language', 'until', 'first', 'contagion', 'during', 'first', 'residents', 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['alcohol', 'exercising', 'economic'] [] [] [] ['interactions'] [] ['support', 'power', 'conflict'] [] [] [] ['medical'] [] ['physical', 'health', 'mental', 'health'] [] [] [] [] ['mobility'] [] ['first'] [] [] [] ['grocery', 'travel', 'transport', 'outdoors', 'recreation'] [] [] [] [] ['figure', 'epidemic', 'psychology', 'twitter', 'during', 'first', 'contagion', 'evolution'] ['different', 'language', 'categories', 'tweets', 'related', 'covid', 'heatmaps', 'represents'] ['language', 'category', 'words', 'expressing', 'anxiety', 'manual', 'coding', 'associated', 'three'] ['psycho', 'social', 'epidemics', 'color', 'represents', 'daily', 'standardized', 'fraction', 'people', 'words', 'related'] ['category', 'values', 'higher', 'average', 'those', 'lower', 'categories'] ['partitioned', 'three', 'groups', 'according', 'psycho', 'social', 'epidemics', 'model', 'morality', 'action'] ['average', 'gradient', 'instantaneous', 'variation', 'language', 'categories', 'peaks', 'gradient', 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'psychology'] ['braun', 'clarke', 'using', 'thematic', 'analysis', 'psychology', 'qualitative', 'research', 'psychology'] [] ['broniatowski', 'jamison', 'alkulaib', 'benton', 'quinn', 'dredze'] ['weaponized', 'health', 'communication', 'twitter', 'russian', 'trolls', 'amplify', 'vaccine', 'debate', 'american', 'journal'] ['public', 'health'] ['brooks', 'webster', 'smith', 'woodland', 'wessely', 'greenberg', 'rubin'] ['psychological', 'impact', 'quarantine', 'reduce', 'rapid', 'review', 'evidence', 'lancet'] ['language', 'communicating', 'threat', 'public', 'discourse', 'springer'] ['lerman', 'ferrara', 'tracking', 'social', 'media', 'discourse', 'about', 'covid', 'pandemic', 'development'] ['public', 'coronavirus', 'twitter', 'public', 'health', 'surveillance', 'e19273'] ['19273'] ['eysenbach', 'pandemics', 'twitter', 'content', 'analysis', 'tweets', 'during'] ['outbreak'] ['aiello', 'varga', 'quercia', 'social', 'dimensions', 'conversations', 'relationships'] ['proceedings', 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'cuddy', 'glick', 'universal', 'dimensions', 'social', 'cognition', 'warmth', 'competence', 'trends'] ['cognitive', 'sciences'] ['frimer', 'schaefer', 'oakes', 'moral', 'actor', 'selfish', 'agent', 'journal', 'personality', 'social', 'psychology'] [] ['liang', 'saroha', 'people', 'react', 'virus', 'outbreaks'] ['twitter', 'computational', 'content', 'analysis', 'american', 'journal', 'infection', 'control'] ['galea', 'merchant', 'lurie', 'mental', 'health', 'consequences', 'covid', 'physical', 'distancing'] ['prevention', 'early', 'intervention', 'internal', 'medicine'] ['liang', 'kruse', 'mapping', 'county', 'level', 'mobility', 'pattern', 'changes', 'united'] ['states', 'response', 'covid', 'sigspatial', 'special'] ['gibbs', 'thematic', 'coding', 'categorizing', 'analyzing', 'qualitative', 'london', 'pages'] ['french', 'gergle', 'oberlander', 'language', 'emotion', 'short', 'texts', 'proceedings'] ['conference', 'computer', 'supported', 'cooperative', 'pages'] ['goffman', 'stigma', 'notes', 'management', 'spoiled', 'identity', 'simon', 'schuster'] ['gonzalez', 'hidalgo', 'barabasi', 'understanding', 'individual', 'human', 'mobility', 'patterns', 'nature'] [] ['graham', 'haidt', 'nosek', 'liberals', 'conservatives', 'different', 'moral', 'foundations'] ['journal', 'personality', 'social', 'psychology'] ['graham', 'haidt', 'koleva', 'motyl', 'wojcik', 'ditto', 'moral', 'foundations', 'theory'] ['pragmatic', 'validity', 'moral', 'pluralism', 'advances', 'experimental', 'social', 'psychology', 'volume', 'pages'] ['elsevier'] ['grossman', 'rexer', 'thirumurthy', 'political', 'partisanship', 'influences', 'behavioral', 'responses'] ['governors', 'recommendations', 'covid', 'prevention', 'united', 'states', 'available', '3578695'] ['hebert', 'clemow', 'pbert', 'ockene', 'ockene', 'social', 'desirability', 'dietary', 'report'] ['compromise', 'validity', 'dietary', 'intake', 'measures', 'international', 'journal', 'epidemiology'] ['hochreiter', 'schmidhuber', 'short', 'memory', 'neural', 'computation'] ['hofstede', 'hofstede', 'minkov', 'cultures', 'organizations', 'software', 'volume'] ['mcgraw'] ['holmes', 'connor', 'perry', 'tracey', 'wessely', 'arseneault', 'ballard', 'christensen'] ['silver', 'everall', 'multidisciplinary', 'research', 'priorities', 'covid', 'pandemic', 'action', 'mental'] ['health', 'science', 'lancet', 'psychiatry'] ['jiang', 'assessment', 'public', 'attention', 'perception', 'emotional'] ['behavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china', 'perception', 'emotional'] ['behavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china'] ['huang', 'bidirectional', 'models', 'sequence', 'tagging', 'arxiv', 'preprint', 'arxiv', '01991'] [] ['auriemma', 'cashaw', 'report', 'underreporting', 'depression', 'journal'] ['personality', 'assessment', 'epidemic', 'psychology', 'works', 'twitter'] ['husnayain', 'applications', 'google', 'search', 'trends', 'communication', 'infectious'] ['disease', 'management', 'study', 'covid', 'outbreak', 'taiwan', 'international', 'journal', 'infectious', 'diseases'] [] ['jimeno', 'yepes', 'mackinlay', 'identifying', 'diseases', 'drugs', 'symptoms', 'twitter'] ['studies', 'health', 'technology', 'informatics'] ['johnson', 'fendrich', 'modeling', 'sources', 'report', 'survey', 'epidemiology', 'annals'] ['epidemiology'] ['tobin', 'massey', 'anderson', 'measuring', 'emotional', 'expression', 'linguistic'] ['inquiry', 'count', 'american', 'journal', 'psychology', 'pages'] ['huremovi', 'psychology', 'pandemic', 'psychiatry', 'pandemics', 'pages', 'springer'] ['kornfield', 'gustafson', 'before', 'relapse'] ['language', 'online', 'discussion', 'forum', 'predicts', 'risky', 'drinking', 'among', 'those', 'recovery', 'health'] ['communication'] ['kouzy', 'jaoude', 'kraitem', 'karam', 'zarka', 'traboulsi'] ['baddour', 'coronavirus', 'viral', 'quantifying', 'covid', 'misinformation', 'epidemic', 'twitter', 'cureus'] [] ['ubler', 'wessler', 'avioli', 'death', 'dying'] ['goodchild', 'spatial', 'temporal', 'socioeconomic', 'patterns', 'twitter', 'flickr'] ['cartography', 'geographic', 'information', 'science'] ['impact', 'covid', 'epidemic', 'declaration', 'psychological'] ['consequences', 'study', 'active', 'weibo', 'users', 'international', 'journal', 'environmental', 'research', 'public', 'health'] [] ['liang', 'effect', 'covid', 'youth', 'mental', 'health'] ['psychiatric', 'quarterly', 'pages'] ['goyal', 'joshi', 'lewis', 'zettlemoyer', 'stoyanov', 'roberta'] ['robustly', 'optimized', 'pretraining', 'approach', 'arxiv', 'preprint', 'arxiv', '11692'] ['utkepohl', 'introduction', 'multiple', 'series', 'analysis', 'springer', 'science', 'business', 'media'] ['maslow', 'theory', 'human', 'motivation', 'psychological', 'review'] ['mcconnell', 'banks', 'avian', 'planning', 'possible', 'pandemic', 'trading', 'technology'] ['mohammad', 'turney', 'crowdsourcing', 'emotion', 'association', 'lexicon', 'computational', 'intelligence'] [] ['moinet', 'pastor', 'satorras', 'barrat', 'effect', 'perception', 'epidemic', 'spreading', 'temporal', 'networks'] ['physical', 'review', '012313'] ['connor', 'balasubramanyan', 'routledge', 'smith', 'tweets', 'polls', 'linking', 'sentiment'] ['public', 'opinion', 'series', 'fourth', 'international', 'conference', 'weblogs', 'social', 'media'] ['oyeyemi', 'gabarron', 'ebola', 'twitter', 'misinformation', 'dangerous', 'combination'] ['g6178'] ['painter', 'political', 'beliefs', 'affect', 'compliance', 'covid', 'social', 'distancing', 'orders', 'available'] ['3569098'] ['palshikar', 'simple', 'algorithms', 'detection', 'series', 'advanced'] ['analysis', 'business', 'analytics', 'intelligence', 'volume'] ['paszke', 'gross', 'chintala', 'chanan', 'devito', 'desmaison', 'antiga', 'lerer'] ['automatic', 'differentiation', 'pytorch', 'proceedings', 'advances', 'neural', 'information', 'processing', 'systems'] ['autodiff', 'workshop', 'epidemic', 'psychology', 'works', 'twitter'] ['pennington', 'socher', 'manning', 'glove', 'global', 'vectors', 'representation', 'proceedings'] ['conference', 'empirical', 'methods', 'natural', 'language', 'processing', 'pages', 'association', 'computational'] ['linguistics'] ['perrin', 'anderson', 'share', 'adults', 'using', 'social', 'media', 'including', 'facebook'] ['mostly', 'unchanged', 'since', 'https', 'pewresearch'] ['share', 'adults', 'using', 'social', 'media', 'including', 'facebook', 'mostly', 'unchanged', 'since'] ['plutchik', 'emotions', 'university', 'press', 'america'] ['pulido', 'villarejo', 'carballido', 'redondo', 'covid', 'infodemic', 'retweets'] ['science', 'based', 'information', 'coronavirus', 'false', 'information', 'international', 'sociology'] ['0268580920914755'] ['nationwide', 'survey', 'psychological', 'distress', 'among', 'chinese'] ['people', 'covid', 'epidemic', 'implications', 'policy', 'recommendations', 'general', 'psychiatry'] ['rezapour', 'diesner', 'enhancing', 'measurement', 'social', 'effects', 'capturing', 'morality'] ['proceedings', 'tenth', 'workshop', 'computational', 'approaches', 'subjectivity', 'sentiment', 'social', 'media'] ['analysis', 'pages'] ['russell', 'mining', 'social', 'mining', 'facebook', 'twitter', 'linkedin', 'google', 'github'] ['reilly', 'media'] ['alani', 'alleviating', 'sparsity', 'twitter', 'sentiment', 'analysis', 'workshop', 'proceedings'] [] ['salathe', 'bengtsson', 'bodnar', 'brewer', 'brownstein', 'buckee', 'campbell', 'cattuto'] ['khandelwal', 'mabry', 'digital', 'epidemiology', 'comput', 'e1002616'] ['scepanovic', 'martin', 'lopez', 'quercia', 'baykaner', 'extracting', 'medical', 'entities', 'social', 'media'] ['proceedings', 'conference', 'health', 'inference', 'learning', 'pages'] ['schutz', 'luckmann', 'zaner', 'engelhardt', 'structures', 'world', 'number', 'northwest', 'university', 'studies', 'phenomenology', 'existential', 'philosophy', 'northwestern', 'university', 'press'] ['9780810106222', 'https', 'books', 'google', 'books', 'lgxbxi0xsh8c'] ['gustafson', 'hawkins', 'cleary', 'mctavish', 'pingree', 'eliason'] ['lumpkins', 'effects', 'prayer', 'religious', 'expression', 'within', 'computer', 'support', 'groups', 'women', 'breast'] ['cancer', 'psycho', 'oncology', 'journal', 'psychological', 'social', 'behavioral', 'dimensions', 'cancer'] [] ['rudzicz', 'detecting', 'anxiety', 'through', 'reddit', 'proceedings', 'fourth', 'workshop', 'compu', 'tational', 'linguistics', 'clinical', 'psychology', 'linguistic', 'signal', 'clinical', 'reality', 'pages'] ['shultz', 'baingana', 'neria', 'ebola', 'outbreak', 'mental', 'health', 'current', 'status', 'recom', 'mended', 'response'] ['simms', 'ramstedt', 'richards', 'martinez', 'giraud', 'carrier', 'detecting', 'cognitive', 'distortions'] ['through', 'machine', 'learning', 'analytics', 'international', 'conference', 'healthcare', 'informatics'] ['pages'] ['smith', 'shinebourne', 'interpretative', 'phenomenological', 'analysis', 'american', 'psychological', 'association'] [] ['sommariva', 'vamos', 'mantzarlis', 'martinez', 'tyson', 'spreading', 'exploring'] ['health', 'messages', 'social', 'media', 'implications', 'health', 'professionals', 'using', 'study', 'american', 'journal'] ['health', 'education'] ['strong', 'epidemic', 'psychology', 'model', 'sociology', 'health', 'illness'] ['tajfel', 'turner', 'austin', 'worchel', 'integrative', 'theory', 'intergroup', 'conflict', 'organizational'] ['identity'] ['tausczik', 'pennebaker', 'psychological', 'meaning', 'words', 'computerized', 'analysis'] ['methods', 'journal', 'language', 'social', 'psychology', 'epidemic', 'psychology', 'works', 'twitter'] ['economist', 'millions', 'stuck', 'online', 'wellness', 'dustry', 'booming', 'https', 'economist', 'international'] ['millions', 'stuck', 'online', 'wellness', 'industry', 'booming'] ['bavel', 'baicker', 'boggio', 'capraro', 'cichocka', 'cikara', 'crockett'] ['douglas', 'druckman', 'using', 'social', 'behavioural', 'science', 'support', 'covid', 'pandemic', 'response'] ['nature', 'human', 'behaviour', 'pages'] ['varol', 'ferrara', 'davis', 'menczer', 'flammini', 'online', 'human', 'interactions', 'detection'] ['estimation', 'characterization', 'eleventh', 'international', 'conference', 'social', 'media'] ['immediate', 'psychological', 'responses', 'associated'] ['factors', 'during', 'initial', 'stage', 'coronavirus', 'disease', 'covid', 'epidemic', 'among', 'general', 'population'] ['china', 'international', 'journal', 'environmental', 'research', 'public', 'health'] ['waterloo', 'baumgartner', 'peter', 'valkenburg', 'norms', 'online', 'expressions', 'emotion'] ['comparing', 'facebook', 'twitter', 'instagram', 'whatsapp', 'media', 'society'] ['windsor', 'dowell', 'graesser', 'language', 'autocrats', 'leaders', 'language', 'natural', 'disaster', 'crises'] ['hazards', 'crisis', 'public', 'policy'] ['wojcik', 'hughes', 'sizing', 'twitter', 'users', 'washington', 'research', 'center'] ['theis', 'kordy', 'language', 'eating', 'disorder', 'blogs', 'psychological', 'implications', 'social', 'online'] ['activity', 'journal', 'language', 'social', 'psychology'] ['propagating', 'debunking', 'conspiracy', 'theories', 'twitter', 'during', 'virus', 'outbreak'] ['cyberpsychology', 'behavior', 'social', 'networking'] ['torres', 'menczer', 'prevalence', 'credibility', 'information', 'twitter', 'during'] ['covid', 'outbreak', 'arxiv', 'preprint', 'arxiv', '14484'] ['acknowledgments'] ['thank', 'sarah', 'konrath', 'rosta', 'farzan', 'licia', 'capra', 'their', 'useful', 'feedback', 'manuscript', 'research'] ['partly', 'supported', 'grant', 'green', 'routes', '869764'] ['availability'] ['daily', 'aggregates', 'measurements', 'available', 'https', 'figshare', '14892642'] ['tweet', 'available', 'https', 'github', 'echen102', 'covid', 'tweetids', 'other', 'datasets'] ['visualizations', 'available', 'project', 'social', 'dynamics', 'epidemicpsychology'] ['competing', 'interests'] ['author', 'declare', 'competing', 'interests', 'epidemic', 'psychology', 'works', 'twitter'] ['supplementary', 'materials'] ['spatial', 'representativeness', 'twitter', 'geographical', 'penetration', 'twitter'] ['reflects', 'spatial', 'distribution', 'population', 'compared', 'activity', 'state'] ['census', 'population', 'figure', 'shows', 'number', 'twitter', 'users', 'volume', 'their', 'tweets'] ['correlate', 'strongly', 'census', 'population', 'population', 'estimates', 'level', 'states'] ['https', 'census'] [] ['population'] [] [] [] [] [] ['tweets'] [] [] [] [] [] ['population'] [] [] [] [] [] [] ['users'] [] [] [] [] ['figure', 'correlation', 'census', 'population', 'states', 'estimated'] ['number', 'tweets', 'posted', 'those', 'states', 'number', 'unique', 'twitter', 'users', 'posted'] ['least', 'tweet', 'right', 'during', 'period', 'analysis', 'linear', 'shown', 'together'] ['coefficient', 'determination', 'linear', 'regression'] ['pearson', 'correlation', 'coefficient'] ['coefficient', 'number', 'tweets', 'twitter', 'users', 'scale', 'almost', 'linearly'] ['population', 'estimates'] ['signals', 'racism', 'twitter', 'discussions', 'corroborate', 'qualitative', 'intuition', 'first', 'phase'] ['characterized', 'discussions', 'depicted', 'pandemic', 'foreign', 'problem', 'often', 'using', 'racist', 'undertones'] ['measured', 'volume', 'tweets', 'containing', 'hashtags', 'clear', 'indicators', 'content', 'either', 'racist'] ['aimed', 'antagonizing', 'china', 'manually', 'selected', 'those', 'hashtags', 'hashtags', 'appeared'] ['least', 'times', 'dataset', 'figure', 'shows', 'normalized', 'volume', 'those', 'hashtags'] ['during', 'first', 'phase', 'related', 'mentions', 'focus', 'twitter', 'discussions', 'china', 'asian'] ['foreigners', 'their', 'peaks'] ['breakdown', 'phases', 'state', 'during', 'period', 'analysis', 'country', 'fragmented', 'regions'] ['dealing', 'different', 'stages', 'outbreak', 'policies', 'contrast', 'varied', 'across', 'states', 'assess'] ['whether', 'those', 'differences', 'impacted', 'unfolding', 'psycho', 'social', 'epidemics', 'across', 'states', 'computed'] ['measures', 'represent', 'relative', 'presence', 'three', 'phases', 'refusal', 'anger', 'acceptance'] ['state', 'individually', 'results', 'shown', 'figure', 'despite', 'minor', 'differences', 'state', 'level', 'curves'] ['indicate', 'three', 'phases', 'place', 'states', 'sequence', 'phases', 'consistent', 'across', 'states'] ['refusal', 'followed', 'anger', 'which', 'followed', 'acceptance', 'there', 'little', 'variation'] ['times', 'transition', 'between', 'phases', 'epidemic', 'psychology', 'works', 'twitter']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'covid': 0.4452922659095092,
'pandem': 0.3598238418087631,
'health': 0.3346202460677652,
'polici': 0.2629476874262243,
'impact': 0.2151343253537285}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph20.png')
# LSA Model
number_of_topics = 20 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_psychology =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_psychology,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled23.png')
cosine-similarity
covid pandem health polici impact
covid 1 0.000928622 -0.0102666 0.00596031 0.0383039
pandem 0.000928622 1 -0.00728671 0.0283235 -0.00412654
health -0.0102666 -0.00728671 1 0.04196 -0.0366029
polici 0.00596031 0.0283235 0.04196 1 0.0375765
impact 0.0383039 -0.00412654 -0.0366029 0.0375765 1